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diff --git a/code/environment/.ipynb_checkpoints/gyroscope_environment_testing-checkpoint.ipynb b/code/environment/.ipynb_checkpoints/gyroscope_environment_testing-checkpoint.ipynb
new file mode 100644
index 0000000..a058745
--- /dev/null
+++ b/code/environment/.ipynb_checkpoints/gyroscope_environment_testing-checkpoint.ipynb
@@ -0,0 +1,367 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Gyroscope Environment Testbed"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "import numpy as np\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self, dt):\n",
+ " \n",
+ " \n",
+ " # Reference angles on theta and phi\n",
+ " self.x1_ref = 0\n",
+ " self.x3_ref = 0\n",
+ " \n",
+ " # Gold disk speed \n",
+ " self.w = 0\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = dt\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 733.04 # rad/s (7000rpm)\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiffAngle = np.pi\n",
+ " self.maxdiskSpeed = 400 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxdiffAngle,self.maxdiffAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, diff_x1, diff_x3, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " reward = diff_x1**2 + diff_x1**2 + .001*(u1**2) + .001*(u2**2)\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " if self.x1_ref is None:\n",
+ " diff_x1 = 0\n",
+ " else:\n",
+ " diff_x1 = angle_normalize(x1-self.x1_ref)\n",
+ " \n",
+ " if self.x3_ref is None:\n",
+ " diff_x3 = 0\n",
+ " else:\n",
+ " diff_x3 = angle_normalize(x3-self.x3_ref)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,diff_x1,diff_x3,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, x_0 = None):\n",
+ " \n",
+ " # Generate random state (for training) or take the given one (for simulation)\n",
+ " if x_0 is None:\n",
+ " state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " state = x_0\n",
+ " \n",
+ " \n",
+ " # Reference angles on theta and phi\n",
+ " self.x1_ref = angle_normalize(state[0] - state[4])\n",
+ " self.x3_ref = angle_normalize(state[2] - state[5])\n",
+ " \n",
+ " # Gold disk speed \n",
+ " self.w = state[-1]\n",
+ " \n",
+ " self.state = state\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = gyro.w\n",
+ " dt = gyro.dt\n",
+ " \n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "# Test parameters\n",
+ "w_rpm = 200\n",
+ "w = w_rpm * 2 * np.pi / 60\n",
+ "x_0 = [0.5, 0.2, 0.5, 0,0,0,w]\n",
+ "x1_ref = 1\n",
+ "x3_ref = 1\n",
+ "dt = .02\n",
+ "\n",
+ "# Create env\n",
+ "env = GyroscopeEnv(dt)\n",
+ "env.seed(2)\n",
+ "state = env.reset(x_0)\n",
+ "\n",
+ "# Test\n",
+ "time = np.arange(0, 5.5, dt)\n",
+ "reward = None\n",
+ "val = []\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " u = [0,0]#env.action_space.sample()\n",
+ " state, reward, done, info = env.step([0,0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1368x1368 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(3,2,figsize=(19,19))\n",
+ "time = np.arange(0, 5.5, 0.02)\n",
+ "plt.subplot(3,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[4] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[5] for row in val],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/code/environment/gyroscope_environment_testing.ipynb b/code/environment/gyroscope_environment_testing.ipynb
new file mode 100644
index 0000000..a058745
--- /dev/null
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Gyroscope Environment Testbed"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "import numpy as np\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self, dt):\n",
+ " \n",
+ " \n",
+ " # Reference angles on theta and phi\n",
+ " self.x1_ref = 0\n",
+ " self.x3_ref = 0\n",
+ " \n",
+ " # Gold disk speed \n",
+ " self.w = 0\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = dt\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 733.04 # rad/s (7000rpm)\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiffAngle = np.pi\n",
+ " self.maxdiskSpeed = 400 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxdiffAngle,self.maxdiffAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, diff_x1, diff_x3, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " reward = diff_x1**2 + diff_x1**2 + .001*(u1**2) + .001*(u2**2)\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " if self.x1_ref is None:\n",
+ " diff_x1 = 0\n",
+ " else:\n",
+ " diff_x1 = angle_normalize(x1-self.x1_ref)\n",
+ " \n",
+ " if self.x3_ref is None:\n",
+ " diff_x3 = 0\n",
+ " else:\n",
+ " diff_x3 = angle_normalize(x3-self.x3_ref)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,diff_x1,diff_x3,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, x_0 = None):\n",
+ " \n",
+ " # Generate random state (for training) or take the given one (for simulation)\n",
+ " if x_0 is None:\n",
+ " state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " state = x_0\n",
+ " \n",
+ " \n",
+ " # Reference angles on theta and phi\n",
+ " self.x1_ref = angle_normalize(state[0] - state[4])\n",
+ " self.x3_ref = angle_normalize(state[2] - state[5])\n",
+ " \n",
+ " # Gold disk speed \n",
+ " self.w = state[-1]\n",
+ " \n",
+ " self.state = state\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = gyro.w\n",
+ " dt = gyro.dt\n",
+ " \n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "# Test parameters\n",
+ "w_rpm = 200\n",
+ "w = w_rpm * 2 * np.pi / 60\n",
+ "x_0 = [0.5, 0.2, 0.5, 0,0,0,w]\n",
+ "x1_ref = 1\n",
+ "x3_ref = 1\n",
+ "dt = .02\n",
+ "\n",
+ "# Create env\n",
+ "env = GyroscopeEnv(dt)\n",
+ "env.seed(2)\n",
+ "state = env.reset(x_0)\n",
+ "\n",
+ "# Test\n",
+ "time = np.arange(0, 5.5, dt)\n",
+ "reward = None\n",
+ "val = []\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " u = [0,0]#env.action_space.sample()\n",
+ " state, reward, done, info = env.step([0,0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1368x1368 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(3,2,figsize=(19,19))\n",
+ "time = np.arange(0, 5.5, 0.02)\n",
+ "plt.subplot(3,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[4] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[5] for row in val],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/code/simulation/.ipynb_checkpoints/gyroscope_simulation-checkpoint.ipynb b/code/simulation/.ipynb_checkpoints/gyroscope_simulation-checkpoint.ipynb
new file mode 100644
index 0000000..a7b4502
--- /dev/null
+++ b/code/simulation/.ipynb_checkpoints/gyroscope_simulation-checkpoint.ipynb
@@ -0,0 +1,515 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from vpython import *\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.image as mpimg\n",
+ "from scipy.integrate import solve_ivp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Frame of reference (cf Agram)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x1080 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f = plt.subplots(figsize=(15,15))\n",
+ "img=mpimg.imread('AgramFrame.png')\n",
+ "imgplot = plt.imshow(img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Constants"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Gold disk speed \n",
+ "w_rpm = 200\n",
+ "w = w_rpm * 2 * np.pi / 60\n",
+ "\n",
+ "# Inertias in Kg*m2\n",
+ "Jbx1 = 0.0019\n",
+ "Jbx2 = 0.0008\n",
+ "Jbx3 = 0.0012\n",
+ "Jrx1 = 0.0179\n",
+ "Jdx1 = 0.0028\n",
+ "Jdx3 = 0.0056\n",
+ "\n",
+ "# Combined inertias\n",
+ "J1 = Jbx1 - Jbx3 + Jdx1 - Jdx3\n",
+ "J2 = Jbx1 + Jdx1 + Jrx1\n",
+ "J3 = Jbx2 + Jdx1\n",
+ "\n",
+ "# Motor constants\n",
+ "Kamp = 0.5 # A/V\n",
+ "Ktorque = 0.0704 # Nm/A\n",
+ "eff = 0.86\n",
+ "nRed = 1.5\n",
+ "nBlue = 1\n",
+ "KtotRed = Kamp*Ktorque*eff*nRed \n",
+ "KtotBlue = Kamp*Ktorque*eff*nBlue "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Non-linear system equations (6 states)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def dx_dt6(t, x, u1, u2, u3):\n",
+ " dx_dt = [0, 0, 0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = ((2*Jbx1-2*Jbx3+2*Jdx1-Jdx3)*np.sin(2*x[2])*x[1]*x[3]+2*(u1-u3*np.sin(x[2])-Jdx3*np.cos(x[2])*x[3]*x[5]))/(Jbx1+Jbx3+Jdx1+2*Jrx1+(Jbx1-Jbx3+Jdx1)*np.cos(2*x[2]))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - (Jbx1-Jbx3+Jdx1-Jdx3)*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*x[5])/(Jbx2+Jdx1)\n",
+ " dx_dt[4] = x[5]\n",
+ " dx_dt[5] = -((-2*Jdx3*np.cos(x[2])*(-3*Jbx1+Jbx3-3*Jdx1+Jdx3-2*Jrx1+Jdx3-2*Jrx1+(Jbx1-Jbx3+Jdx1-Jdx3)*np.cos(2*x[2]))*x[1]*x[3]-2*((Jbx1+Jbx3+Jdx1+Jdx3+2*Jrx1+(Jbx1-Jbx3+Jdx1-Jdx3)*np.cos([2*x[2]]))*u3-2*Jdx3*u1*np.sin(x[2])+Jdx3*Jdx3*np.sin(2*x[2])*x[3]*x[5]))/(2*Jdx3*(Jbx1+Jbx3+Jdx1+2*Jrx1+(Jbx1-Jbx3+Jdx1)*np.cos(2*x[2]))))\n",
+ " return dx_dt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initial conditions: [theta, theta dot, phi, phi dot, psi, psi dot]\n",
+ "x_0 = [0.5, 0.2, 0.5, 0, 0, w]\n",
+ "\n",
+ "# Time step in sec and time vector\n",
+ "dt = 0.01\n",
+ "time = np.arange(0, 5.5, dt)\n",
+ "\n",
+ "# Input\n",
+ "val=[]\n",
+ "for k in range(len(time)):\n",
+ " u3 = 0.0005*(w-x_0[5]) # to control disk with P controller as done on machine\n",
+ " results = solve_ivp(dx_dt6, (0, dt), x_0, method='RK45', args=(KtotRed*0,KtotBlue*0,u3))\n",
+ " x = [row[-1] for row in results.y] \n",
+ " x_0 = x\n",
+ " val.append(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x1440 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(3,2,figsize=(15,20))\n",
+ "\n",
+ "plt.subplot(3,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,5)\n",
+ "plt.title('Disk angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\psi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[4] for row in val],'y-')\n",
+ "\n",
+ "plt.subplot(3,2,6)\n",
+ "plt.title('Disk speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\psi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[5] for row in val],'y-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3D Rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glow.min\");}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glowcomm\");}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/jquery-ui.custom.min\");}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glow.min\"], function(){console.log(\"GLOW LOADED\");});}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glowcomm\"], function(){console.log(\"GLOWCOMM LOADED\");});}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/jquery-ui.custom.min\"], function(){console.log(\"JQUERY LOADED\");});}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "t = 0\n",
+ "dt = 0.01\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 200\n",
+ "\n",
+ "# 6.4/100\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simplified non-linear system equations (4 states)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def dx_dt4(t, x, u1, u2):\n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "# Initial conditions: [theta, theta dot, phi, phi dot]\n",
+ "x_0 = [0.5, 0.2, 0.5, 0]\n",
+ "\n",
+ "# Time step in sec and time vector\n",
+ "dt = 0.02\n",
+ "time = np.arange(0, 5.5, dt)\n",
+ "\n",
+ "# Input\n",
+ "u1 = 0\n",
+ "u2 = 0 \n",
+ "val=[]\n",
+ "for k in range(len(time)):\n",
+ " results = solve_ivp(dx_dt4, (0, 0+dt), x_0, method='RK45', args=(KtotRed*u1,KtotBlue*u2))\n",
+ " x = [row[-1] for row in results.y] \n",
+ " x_0 = x\n",
+ " val.append(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x1080 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(2,2,figsize=(15,15))\n",
+ "\n",
+ "plt.subplot(2,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(2,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(2,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(2,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/code/simulation/AgramFrame.png b/code/simulation/AgramFrame.png
new file mode 100644
index 0000000..d7dec77
Binary files /dev/null and b/code/simulation/AgramFrame.png differ
diff --git a/code/simulation/gyroscope_simulation.ipynb b/code/simulation/gyroscope_simulation.ipynb
new file mode 100644
index 0000000..a7b4502
--- /dev/null
+++ b/code/simulation/gyroscope_simulation.ipynb
@@ -0,0 +1,515 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from vpython import *\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.image as mpimg\n",
+ "from scipy.integrate import solve_ivp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Frame of reference (cf Agram)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x1080 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f = plt.subplots(figsize=(15,15))\n",
+ "img=mpimg.imread('AgramFrame.png')\n",
+ "imgplot = plt.imshow(img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Constants"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Gold disk speed \n",
+ "w_rpm = 200\n",
+ "w = w_rpm * 2 * np.pi / 60\n",
+ "\n",
+ "# Inertias in Kg*m2\n",
+ "Jbx1 = 0.0019\n",
+ "Jbx2 = 0.0008\n",
+ "Jbx3 = 0.0012\n",
+ "Jrx1 = 0.0179\n",
+ "Jdx1 = 0.0028\n",
+ "Jdx3 = 0.0056\n",
+ "\n",
+ "# Combined inertias\n",
+ "J1 = Jbx1 - Jbx3 + Jdx1 - Jdx3\n",
+ "J2 = Jbx1 + Jdx1 + Jrx1\n",
+ "J3 = Jbx2 + Jdx1\n",
+ "\n",
+ "# Motor constants\n",
+ "Kamp = 0.5 # A/V\n",
+ "Ktorque = 0.0704 # Nm/A\n",
+ "eff = 0.86\n",
+ "nRed = 1.5\n",
+ "nBlue = 1\n",
+ "KtotRed = Kamp*Ktorque*eff*nRed \n",
+ "KtotBlue = Kamp*Ktorque*eff*nBlue "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Non-linear system equations (6 states)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def dx_dt6(t, x, u1, u2, u3):\n",
+ " dx_dt = [0, 0, 0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = ((2*Jbx1-2*Jbx3+2*Jdx1-Jdx3)*np.sin(2*x[2])*x[1]*x[3]+2*(u1-u3*np.sin(x[2])-Jdx3*np.cos(x[2])*x[3]*x[5]))/(Jbx1+Jbx3+Jdx1+2*Jrx1+(Jbx1-Jbx3+Jdx1)*np.cos(2*x[2]))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - (Jbx1-Jbx3+Jdx1-Jdx3)*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*x[5])/(Jbx2+Jdx1)\n",
+ " dx_dt[4] = x[5]\n",
+ " dx_dt[5] = -((-2*Jdx3*np.cos(x[2])*(-3*Jbx1+Jbx3-3*Jdx1+Jdx3-2*Jrx1+Jdx3-2*Jrx1+(Jbx1-Jbx3+Jdx1-Jdx3)*np.cos(2*x[2]))*x[1]*x[3]-2*((Jbx1+Jbx3+Jdx1+Jdx3+2*Jrx1+(Jbx1-Jbx3+Jdx1-Jdx3)*np.cos([2*x[2]]))*u3-2*Jdx3*u1*np.sin(x[2])+Jdx3*Jdx3*np.sin(2*x[2])*x[3]*x[5]))/(2*Jdx3*(Jbx1+Jbx3+Jdx1+2*Jrx1+(Jbx1-Jbx3+Jdx1)*np.cos(2*x[2]))))\n",
+ " return dx_dt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initial conditions: [theta, theta dot, phi, phi dot, psi, psi dot]\n",
+ "x_0 = [0.5, 0.2, 0.5, 0, 0, w]\n",
+ "\n",
+ "# Time step in sec and time vector\n",
+ "dt = 0.01\n",
+ "time = np.arange(0, 5.5, dt)\n",
+ "\n",
+ "# Input\n",
+ "val=[]\n",
+ "for k in range(len(time)):\n",
+ " u3 = 0.0005*(w-x_0[5]) # to control disk with P controller as done on machine\n",
+ " results = solve_ivp(dx_dt6, (0, dt), x_0, method='RK45', args=(KtotRed*0,KtotBlue*0,u3))\n",
+ " x = [row[-1] for row in results.y] \n",
+ " x_0 = x\n",
+ " val.append(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x1440 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(3,2,figsize=(15,20))\n",
+ "\n",
+ "plt.subplot(3,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,5)\n",
+ "plt.title('Disk angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\psi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[4] for row in val],'y-')\n",
+ "\n",
+ "plt.subplot(3,2,6)\n",
+ "plt.title('Disk speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\psi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[5] for row in val],'y-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3D Rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glow.min\");}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glowcomm\");}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/jquery-ui.custom.min\");}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glow.min\"], function(){console.log(\"GLOW LOADED\");});}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glowcomm\"], function(){console.log(\"GLOWCOMM LOADED\");});}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/jquery-ui.custom.min\"], function(){console.log(\"JQUERY LOADED\");});}else{element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "t = 0\n",
+ "dt = 0.01\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 200\n",
+ "\n",
+ "# 6.4/100\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simplified non-linear system equations (4 states)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def dx_dt4(t, x, u1, u2):\n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "# Initial conditions: [theta, theta dot, phi, phi dot]\n",
+ "x_0 = [0.5, 0.2, 0.5, 0]\n",
+ "\n",
+ "# Time step in sec and time vector\n",
+ "dt = 0.02\n",
+ "time = np.arange(0, 5.5, dt)\n",
+ "\n",
+ "# Input\n",
+ "u1 = 0\n",
+ "u2 = 0 \n",
+ "val=[]\n",
+ "for k in range(len(time)):\n",
+ " results = solve_ivp(dx_dt4, (0, 0+dt), x_0, method='RK45', args=(KtotRed*u1,KtotBlue*u2))\n",
+ " x = [row[-1] for row in results.y] \n",
+ " x_0 = x\n",
+ " val.append(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x1080 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(2,2,figsize=(15,15))\n",
+ "\n",
+ "plt.subplot(2,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(2,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(2,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(2,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/code/training_spinuplib/.ipynb_checkpoints/gyroscope_ddpg_spinup_v0-checkpoint.ipynb b/code/training_spinuplib/.ipynb_checkpoints/gyroscope_ddpg_spinup_v0-checkpoint.ipynb
new file mode 100644
index 0000000..18d7b7d
--- /dev/null
+++ b/code/training_spinuplib/.ipynb_checkpoints/gyroscope_ddpg_spinup_v0-checkpoint.ipynb
@@ -0,0 +1,3803 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "x83dMPapQBN6"
+ },
+ "source": [
+ "# Gyroscope DDPG Testbed (udacity library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8inmkEG5QHqx"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 655,
+ "status": "ok",
+ "timestamp": 1584030129316,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "DnnJGcnsQvZC",
+ "outputId": "9d6d1408-da29-44ed-af74-4ebdc2bd865a"
+ },
+ "outputs": [],
+ "source": [
+ "cd drive/My\\ Drive/Colab Notebooks/DDPG_uda_v0/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "fuJhdd479TpP"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from collections import deque\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "from vpython import *\n",
+ "\n",
+ "import spinup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "O0O0t5ZR9Tp6"
+ },
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Al_k1rtvQBOM"
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self):\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = 0.05\n",
+ " \n",
+ " # Error\n",
+ " self.int_diff_x1 = 0\n",
+ " self.int_diff_x3 = 0\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 100 * 2 * np.pi / 60\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiskSpeed = 300 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxAngle,self.maxAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, x1_ref, x3_ref, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " # Angle error\n",
+ " diff_x1 = angle_normalize(x1 - x1_ref)\n",
+ " diff_x3 = angle_normalize(x3 - x3_ref)\n",
+ " \n",
+ " # Integral of error\n",
+ " self.int_diff_x1 = self.int_diff_x1 + diff_x1\n",
+ " self.int_diff_x3 = self.int_diff_x3 + diff_x3\n",
+ " \n",
+ " # Reward 1: differentiable reward (LQR obj function)\n",
+ " reward = -((3*diff_x1)**2 + (3*diff_x3)**2 + (.2*x2)**2 + (.2*x4)**2 + (.1*u1)**2 + (.1*u2)**2)\\\n",
+ " #-(0.01*abs(self.int_diff_x1) + 0.01*abs(self.int_diff_x3))\n",
+ "\n",
+ " \"\"\"# Count time spent in goal:\n",
+ " if abs(diff_x1)<0.05 and abs(diff_x3)<0.05:\n",
+ " self.countGoal +=1\n",
+ " else:\n",
+ " self.countGoal = 0\n",
+ " \n",
+ " # Reward 2: sparse reward for staying in goal range for a long time \n",
+ " if self.countGoal >= (self.timeGoal)/self.dt: #max expected reward over length becomes 0 + (totaltime-goaltime)\n",
+ " reward += 1\"\"\"\n",
+ "\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,x1_ref, x3_ref,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, state = None):\n",
+ " \n",
+ " \n",
+ " # Generate random state (for training) or use given state (for simulation)\n",
+ " if state is None:\n",
+ " self.state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " self.state = state\n",
+ "\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = x[-1]\n",
+ "\n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "be0wYIeBQBOc"
+ },
+ "source": [
+ "## Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 533
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 654004,
+ "status": "error",
+ "timestamp": 1584037207187,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "fLyFHs0yQBOd",
+ "outputId": "260489ff-5e40-416a-e529-5a0cfcaefceb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Log dir model already exists! Storing info there anyway.\n",
+ "\u001b[32;1mLogging data to model/progress.txt\u001b[0m\n",
+ "\u001b[36;1mSaving config:\n",
+ "\u001b[0m\n",
+ "{\n",
+ " \"ac_kwargs\":\t{},\n",
+ " \"act_noise\":\t0.1,\n",
+ " \"actor_critic\":\t\"MLPActorCritic\",\n",
+ " \"batch_size\":\t100,\n",
+ " \"env_fn\":\t\"GyroscopeEnv\",\n",
+ " \"epochs\":\t120,\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"gamma\":\t0.95,\n",
+ " \"logger\":\t{\n",
+ " \"<spinup.utils.logx.EpochLogger object at 0x7f7e800ca588>\":\t{\n",
+ " \"epoch_dict\":\t{},\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"first_row\":\ttrue,\n",
+ " \"log_current_row\":\t{},\n",
+ " \"log_headers\":\t[],\n",
+ " \"output_dir\":\t\"model\",\n",
+ " \"output_file\":\t{\n",
+ " \"<_io.TextIOWrapper name='model/progress.txt' mode='w' encoding='UTF-8'>\":\t{\n",
+ " \"mode\":\t\"w\"\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"logger_kwargs\":\t{\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"output_dir\":\t\"model\"\n",
+ " },\n",
+ " \"max_ep_len\":\t110,\n",
+ " \"noise_clip\":\t0.5,\n",
+ " \"num_test_episodes\":\t10,\n",
+ " \"pi_lr\":\t0.001,\n",
+ " \"policy_delay\":\t2,\n",
+ " \"polyak\":\t0.995,\n",
+ " \"q_lr\":\t0.001,\n",
+ " \"replay_size\":\t1000000,\n",
+ " \"save_freq\":\t1,\n",
+ " \"seed\":\t0,\n",
+ " \"start_steps\":\t20000,\n",
+ " \"steps_per_epoch\":\t2200,\n",
+ " \"target_noise\":\t0.2,\n",
+ " \"update_after\":\t800,\n",
+ " \"update_every\":\t50\n",
+ "}\n",
+ "\u001b[32;1m\n",
+ "Number of parameters: \t pi: 68354, \t q1: 68609, \t q2: 68609\n",
+ "\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 1 |\n",
+ "| AverageEpRet | -6.44e+03 |\n",
+ "| StdEpRet | 1.49e+03 |\n",
+ "| MaxEpRet | -4.55e+03 |\n",
+ "| MinEpRet | -9.52e+03 |\n",
+ "| AverageTestEpRet | -7.13e+03 |\n",
+ "| StdTestEpRet | 1.25e+03 |\n",
+ "| MaxTestEpRet | -4.01e+03 |\n",
+ "| MinTestEpRet | -8.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+03 |\n",
+ "| AverageQ1Vals | -123 |\n",
+ "| StdQ1Vals | 54.5 |\n",
+ "| MaxQ1Vals | 0.0809 |\n",
+ "| MinQ1Vals | -364 |\n",
+ "| AverageQ2Vals | -123 |\n",
+ "| StdQ2Vals | 54.6 |\n",
+ "| MaxQ2Vals | 1.77 |\n",
+ "| MinQ2Vals | -373 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 1.95e+03 |\n",
+ "| Time | 15.8 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 2 |\n",
+ "| AverageEpRet | -6.72e+03 |\n",
+ "| StdEpRet | 1.81e+03 |\n",
+ "| MaxEpRet | -4.58e+03 |\n",
+ "| MinEpRet | -1.07e+04 |\n",
+ "| AverageTestEpRet | -8.21e+03 |\n",
+ "| StdTestEpRet | 1.1e+03 |\n",
+ "| MaxTestEpRet | -6.46e+03 |\n",
+ "| MinTestEpRet | -1.01e+04 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+03 |\n",
+ "| AverageQ1Vals | -276 |\n",
+ "| StdQ1Vals | 98.9 |\n",
+ "| MaxQ1Vals | -54.4 |\n",
+ "| MinQ1Vals | -672 |\n",
+ "| AverageQ2Vals | -276 |\n",
+ "| StdQ2Vals | 99.2 |\n",
+ "| MaxQ2Vals | -51.6 |\n",
+ "| MinQ2Vals | -686 |\n",
+ "| LossPi | 256 |\n",
+ "| LossQ | 2.12e+03 |\n",
+ "| Time | 42.1 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 3 |\n",
+ "| AverageEpRet | -7.08e+03 |\n",
+ "| StdEpRet | 1.22e+03 |\n",
+ "| MaxEpRet | -4.94e+03 |\n",
+ "| MinEpRet | -9.27e+03 |\n",
+ "| AverageTestEpRet | -5.91e+03 |\n",
+ "| StdTestEpRet | 1.83e+03 |\n",
+ "| MaxTestEpRet | -3.79e+03 |\n",
+ "| MinTestEpRet | -9.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+03 |\n",
+ "| AverageQ1Vals | -414 |\n",
+ "| StdQ1Vals | 128 |\n",
+ "| MaxQ1Vals | -36.3 |\n",
+ "| MinQ1Vals | -952 |\n",
+ "| AverageQ2Vals | -414 |\n",
+ "| StdQ2Vals | 128 |\n",
+ "| MaxQ2Vals | -26.8 |\n",
+ "| MinQ2Vals | -950 |\n",
+ "| LossPi | 382 |\n",
+ "| LossQ | 2.76e+03 |\n",
+ "| Time | 67.9 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 4 |\n",
+ "| AverageEpRet | -7.06e+03 |\n",
+ "| StdEpRet | 1.53e+03 |\n",
+ "| MaxEpRet | -4.98e+03 |\n",
+ "| MinEpRet | -1.09e+04 |\n",
+ "| AverageTestEpRet | -5.01e+03 |\n",
+ "| StdTestEpRet | 1.18e+03 |\n",
+ "| MaxTestEpRet | -3.34e+03 |\n",
+ "| MinTestEpRet | -6.44e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+03 |\n",
+ "| AverageQ1Vals | -501 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 11.2 |\n",
+ "| MinQ1Vals | -1.07e+03 |\n",
+ "| AverageQ2Vals | -501 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 16.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 459 |\n",
+ "| LossQ | 3.94e+03 |\n",
+ "| Time | 92 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 5 |\n",
+ "| AverageEpRet | -6.3e+03 |\n",
+ "| StdEpRet | 1.14e+03 |\n",
+ "| MaxEpRet | -3.87e+03 |\n",
+ "| MinEpRet | -8.1e+03 |\n",
+ "| AverageTestEpRet | -4.27e+03 |\n",
+ "| StdTestEpRet | 1.52e+03 |\n",
+ "| MaxTestEpRet | -1.82e+03 |\n",
+ "| MinTestEpRet | -6.62e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+04 |\n",
+ "| AverageQ1Vals | -552 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | -15.7 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -552 |\n",
+ "| StdQ2Vals | 172 |\n",
+ "| MaxQ2Vals | 17.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 508 |\n",
+ "| LossQ | 4.63e+03 |\n",
+ "| Time | 116 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 6 |\n",
+ "| AverageEpRet | -6.76e+03 |\n",
+ "| StdEpRet | 1.35e+03 |\n",
+ "| MaxEpRet | -4.61e+03 |\n",
+ "| MinEpRet | -1.13e+04 |\n",
+ "| AverageTestEpRet | -3.67e+03 |\n",
+ "| StdTestEpRet | 994 |\n",
+ "| MaxTestEpRet | -2.24e+03 |\n",
+ "| MinTestEpRet | -5.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+04 |\n",
+ "| AverageQ1Vals | -574 |\n",
+ "| StdQ1Vals | 186 |\n",
+ "| MaxQ1Vals | -39.4 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -574 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | -27.9 |\n",
+ "| MinQ2Vals | -1.29e+03 |\n",
+ "| LossPi | 527 |\n",
+ "| LossQ | 5.18e+03 |\n",
+ "| Time | 139 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 7 |\n",
+ "| AverageEpRet | -7.02e+03 |\n",
+ "| StdEpRet | 1.79e+03 |\n",
+ "| MaxEpRet | -3.34e+03 |\n",
+ "| MinEpRet | -9.5e+03 |\n",
+ "| AverageTestEpRet | -3.05e+03 |\n",
+ "| StdTestEpRet | 1.56e+03 |\n",
+ "| MaxTestEpRet | -1.34e+03 |\n",
+ "| MinTestEpRet | -6.7e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+04 |\n",
+ "| AverageQ1Vals | -582 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | -68.9 |\n",
+ "| MinQ1Vals | -1.38e+03 |\n",
+ "| AverageQ2Vals | -582 |\n",
+ "| StdQ2Vals | 195 |\n",
+ "| MaxQ2Vals | -43.9 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 534 |\n",
+ "| LossQ | 5.26e+03 |\n",
+ "| Time | 163 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 8 |\n",
+ "| AverageEpRet | -6.62e+03 |\n",
+ "| StdEpRet | 1.42e+03 |\n",
+ "| MaxEpRet | -4.04e+03 |\n",
+ "| MinEpRet | -9.02e+03 |\n",
+ "| AverageTestEpRet | -1.54e+03 |\n",
+ "| StdTestEpRet | 771 |\n",
+ "| MaxTestEpRet | -595 |\n",
+ "| MinTestEpRet | -3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+04 |\n",
+ "| AverageQ1Vals | -573 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | -4.68 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -573 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 22.9 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 522 |\n",
+ "| LossQ | 5.51e+03 |\n",
+ "| Time | 187 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 9 |\n",
+ "| AverageEpRet | -6.7e+03 |\n",
+ "| StdEpRet | 1.58e+03 |\n",
+ "| MaxEpRet | -4.21e+03 |\n",
+ "| MinEpRet | -9.46e+03 |\n",
+ "| AverageTestEpRet | -842 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -411 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+04 |\n",
+ "| AverageQ1Vals | -546 |\n",
+ "| StdQ1Vals | 223 |\n",
+ "| MaxQ1Vals | 71.2 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -546 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 60.5 |\n",
+ "| MinQ2Vals | -1.41e+03 |\n",
+ "| LossPi | 490 |\n",
+ "| LossQ | 5.83e+03 |\n",
+ "| Time | 211 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 10 |\n",
+ "| AverageEpRet | -1.66e+03 |\n",
+ "| StdEpRet | 1.99e+03 |\n",
+ "| MaxEpRet | -153 |\n",
+ "| MinEpRet | -8.64e+03 |\n",
+ "| AverageTestEpRet | -872 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -487 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+04 |\n",
+ "| AverageQ1Vals | -488 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 151 |\n",
+ "| MinQ1Vals | -1.43e+03 |\n",
+ "| AverageQ2Vals | -488 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 127 |\n",
+ "| MinQ2Vals | -1.4e+03 |\n",
+ "| LossPi | 432 |\n",
+ "| LossQ | 5.85e+03 |\n",
+ "| Time | 239 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 11 |\n",
+ "| AverageEpRet | -947 |\n",
+ "| StdEpRet | 493 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.89e+03 |\n",
+ "| AverageTestEpRet | -1.09e+03 |\n",
+ "| StdTestEpRet | 661 |\n",
+ "| MaxTestEpRet | -271 |\n",
+ "| MinTestEpRet | -2.78e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+04 |\n",
+ "| AverageQ1Vals | -425 |\n",
+ "| StdQ1Vals | 258 |\n",
+ "| MaxQ1Vals | 141 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -425 |\n",
+ "| StdQ2Vals | 258 |\n",
+ "| MaxQ2Vals | 137 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 376 |\n",
+ "| LossQ | 4.71e+03 |\n",
+ "| Time | 263 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 12 |\n",
+ "| AverageEpRet | -888 |\n",
+ "| StdEpRet | 472 |\n",
+ "| MaxEpRet | -325 |\n",
+ "| MinEpRet | -2.33e+03 |\n",
+ "| AverageTestEpRet | -602 |\n",
+ "| StdTestEpRet | 199 |\n",
+ "| MaxTestEpRet | -286 |\n",
+ "| MinTestEpRet | -910 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+04 |\n",
+ "| AverageQ1Vals | -384 |\n",
+ "| StdQ1Vals | 264 |\n",
+ "| MaxQ1Vals | 161 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -384 |\n",
+ "| StdQ2Vals | 264 |\n",
+ "| MaxQ2Vals | 142 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 341 |\n",
+ "| LossQ | 3.72e+03 |\n",
+ "| Time | 288 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 13 |\n",
+ "| AverageEpRet | -797 |\n",
+ "| StdEpRet | 536 |\n",
+ "| MaxEpRet | -48.6 |\n",
+ "| MinEpRet | -2.46e+03 |\n",
+ "| AverageTestEpRet | -857 |\n",
+ "| StdTestEpRet | 354 |\n",
+ "| MaxTestEpRet | -467 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.86e+04 |\n",
+ "| AverageQ1Vals | -354 |\n",
+ "| StdQ1Vals | 270 |\n",
+ "| MaxQ1Vals | 169 |\n",
+ "| MinQ1Vals | -1.36e+03 |\n",
+ "| AverageQ2Vals | -354 |\n",
+ "| StdQ2Vals | 270 |\n",
+ "| MaxQ2Vals | 166 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 314 |\n",
+ "| LossQ | 3.07e+03 |\n",
+ "| Time | 312 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 14 |\n",
+ "| AverageEpRet | -926 |\n",
+ "| StdEpRet | 418 |\n",
+ "| MaxEpRet | -333 |\n",
+ "| MinEpRet | -1.67e+03 |\n",
+ "| AverageTestEpRet | -902 |\n",
+ "| StdTestEpRet | 481 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -2.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.08e+04 |\n",
+ "| AverageQ1Vals | -326 |\n",
+ "| StdQ1Vals | 274 |\n",
+ "| MaxQ1Vals | 192 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -326 |\n",
+ "| StdQ2Vals | 274 |\n",
+ "| MaxQ2Vals | 191 |\n",
+ "| MinQ2Vals | -1.34e+03 |\n",
+ "| LossPi | 288 |\n",
+ "| LossQ | 2.66e+03 |\n",
+ "| Time | 337 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 15 |\n",
+ "| AverageEpRet | -738 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -267 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -1.45e+03 |\n",
+ "| StdTestEpRet | 1.42e+03 |\n",
+ "| MaxTestEpRet | -185 |\n",
+ "| MinTestEpRet | -4.34e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.3e+04 |\n",
+ "| AverageQ1Vals | -297 |\n",
+ "| StdQ1Vals | 279 |\n",
+ "| MaxQ1Vals | 220 |\n",
+ "| MinQ1Vals | -1.31e+03 |\n",
+ "| AverageQ2Vals | -297 |\n",
+ "| StdQ2Vals | 279 |\n",
+ "| MaxQ2Vals | 221 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 261 |\n",
+ "| LossQ | 2.37e+03 |\n",
+ "| Time | 362 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 16 |\n",
+ "| AverageEpRet | -1.37e+03 |\n",
+ "| StdEpRet | 1.85e+03 |\n",
+ "| MaxEpRet | -223 |\n",
+ "| MinEpRet | -8.63e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 159 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -913 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.52e+04 |\n",
+ "| AverageQ1Vals | -267 |\n",
+ "| StdQ1Vals | 277 |\n",
+ "| MaxQ1Vals | 279 |\n",
+ "| MinQ1Vals | -1.3e+03 |\n",
+ "| AverageQ2Vals | -267 |\n",
+ "| StdQ2Vals | 277 |\n",
+ "| MaxQ2Vals | 269 |\n",
+ "| MinQ2Vals | -1.27e+03 |\n",
+ "| LossPi | 231 |\n",
+ "| LossQ | 2.21e+03 |\n",
+ "| Time | 386 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 17 |\n",
+ "| AverageEpRet | -754 |\n",
+ "| StdEpRet | 595 |\n",
+ "| MaxEpRet | -202 |\n",
+ "| MinEpRet | -2.5e+03 |\n",
+ "| AverageTestEpRet | -636 |\n",
+ "| StdTestEpRet | 389 |\n",
+ "| MaxTestEpRet | -147 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.74e+04 |\n",
+ "| AverageQ1Vals | -245 |\n",
+ "| StdQ1Vals | 273 |\n",
+ "| MaxQ1Vals | 278 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -245 |\n",
+ "| StdQ2Vals | 273 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 211 |\n",
+ "| LossQ | 2.03e+03 |\n",
+ "| Time | 411 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 18 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -241 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -713 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -419 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.96e+04 |\n",
+ "| AverageQ1Vals | -226 |\n",
+ "| StdQ1Vals | 268 |\n",
+ "| MaxQ1Vals | 275 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -226 |\n",
+ "| StdQ2Vals | 268 |\n",
+ "| MaxQ2Vals | 265 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 194 |\n",
+ "| LossQ | 1.83e+03 |\n",
+ "| Time | 434 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 19 |\n",
+ "| AverageEpRet | -745 |\n",
+ "| StdEpRet | 537 |\n",
+ "| MaxEpRet | -109 |\n",
+ "| MinEpRet | -2.14e+03 |\n",
+ "| AverageTestEpRet | -963 |\n",
+ "| StdTestEpRet | 641 |\n",
+ "| MaxTestEpRet | -159 |\n",
+ "| MinTestEpRet | -2.65e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.18e+04 |\n",
+ "| AverageQ1Vals | -214 |\n",
+ "| StdQ1Vals | 263 |\n",
+ "| MaxQ1Vals | 273 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -214 |\n",
+ "| StdQ2Vals | 263 |\n",
+ "| MaxQ2Vals | 266 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 183 |\n",
+ "| LossQ | 1.67e+03 |\n",
+ "| Time | 458 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 20 |\n",
+ "| AverageEpRet | -711 |\n",
+ "| StdEpRet | 511 |\n",
+ "| MaxEpRet | -18.3 |\n",
+ "| MinEpRet | -1.83e+03 |\n",
+ "| AverageTestEpRet | -803 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -126 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+04 |\n",
+ "| AverageQ1Vals | -198 |\n",
+ "| StdQ1Vals | 260 |\n",
+ "| MaxQ1Vals | 276 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -198 |\n",
+ "| StdQ2Vals | 260 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 169 |\n",
+ "| LossQ | 1.57e+03 |\n",
+ "| Time | 483 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 21 |\n",
+ "| AverageEpRet | -664 |\n",
+ "| StdEpRet | 403 |\n",
+ "| MaxEpRet | -176 |\n",
+ "| MinEpRet | -1.87e+03 |\n",
+ "| AverageTestEpRet | -577 |\n",
+ "| StdTestEpRet | 343 |\n",
+ "| MaxTestEpRet | -133 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.62e+04 |\n",
+ "| AverageQ1Vals | -187 |\n",
+ "| StdQ1Vals | 254 |\n",
+ "| MaxQ1Vals | 269 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -187 |\n",
+ "| StdQ2Vals | 254 |\n",
+ "| MaxQ2Vals | 262 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 159 |\n",
+ "| LossQ | 1.48e+03 |\n",
+ "| Time | 507 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 22 |\n",
+ "| AverageEpRet | -662 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -261 |\n",
+ "| MinEpRet | -1.64e+03 |\n",
+ "| AverageTestEpRet | -780 |\n",
+ "| StdTestEpRet | 458 |\n",
+ "| MaxTestEpRet | -39.9 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.84e+04 |\n",
+ "| AverageQ1Vals | -178 |\n",
+ "| StdQ1Vals | 249 |\n",
+ "| MaxQ1Vals | 260 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -178 |\n",
+ "| StdQ2Vals | 249 |\n",
+ "| MaxQ2Vals | 258 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 151 |\n",
+ "| LossQ | 1.35e+03 |\n",
+ "| Time | 531 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 23 |\n",
+ "| AverageEpRet | -524 |\n",
+ "| StdEpRet | 312 |\n",
+ "| MaxEpRet | -85.9 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -768 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -329 |\n",
+ "| MinTestEpRet | -1.3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.06e+04 |\n",
+ "| AverageQ1Vals | -171 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 239 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -170 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 246 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 145 |\n",
+ "| LossQ | 1.29e+03 |\n",
+ "| Time | 555 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 24 |\n",
+ "| AverageEpRet | -714 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -181 |\n",
+ "| MinEpRet | -1.29e+03 |\n",
+ "| AverageTestEpRet | -433 |\n",
+ "| StdTestEpRet | 184 |\n",
+ "| MaxTestEpRet | -178 |\n",
+ "| MinTestEpRet | -786 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.28e+04 |\n",
+ "| AverageQ1Vals | -164 |\n",
+ "| StdQ1Vals | 238 |\n",
+ "| MaxQ1Vals | 227 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -164 |\n",
+ "| StdQ2Vals | 238 |\n",
+ "| MaxQ2Vals | 229 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 139 |\n",
+ "| LossQ | 1.22e+03 |\n",
+ "| Time | 579 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 25 |\n",
+ "| AverageEpRet | -709 |\n",
+ "| StdEpRet | 425 |\n",
+ "| MaxEpRet | -88.8 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -808 |\n",
+ "| StdTestEpRet | 476 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.81e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.5e+04 |\n",
+ "| AverageQ1Vals | -159 |\n",
+ "| StdQ1Vals | 233 |\n",
+ "| MaxQ1Vals | 209 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -159 |\n",
+ "| StdQ2Vals | 233 |\n",
+ "| MaxQ2Vals | 209 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.15e+03 |\n",
+ "| Time | 603 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 26 |\n",
+ "| AverageEpRet | -813 |\n",
+ "| StdEpRet | 433 |\n",
+ "| MaxEpRet | -247 |\n",
+ "| MinEpRet | -1.81e+03 |\n",
+ "| AverageTestEpRet | -702 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.72e+04 |\n",
+ "| AverageQ1Vals | -158 |\n",
+ "| StdQ1Vals | 231 |\n",
+ "| MaxQ1Vals | 194 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -158 |\n",
+ "| StdQ2Vals | 231 |\n",
+ "| MaxQ2Vals | 198 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.14e+03 |\n",
+ "| Time | 628 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 27 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 282 |\n",
+ "| MaxEpRet | -336 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -934 |\n",
+ "| StdTestEpRet | 626 |\n",
+ "| MaxTestEpRet | -317 |\n",
+ "| MinTestEpRet | -2.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.94e+04 |\n",
+ "| AverageQ1Vals | -157 |\n",
+ "| StdQ1Vals | 229 |\n",
+ "| MaxQ1Vals | 190 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -157 |\n",
+ "| StdQ2Vals | 229 |\n",
+ "| MaxQ2Vals | 192 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 135 |\n",
+ "| LossQ | 1.03e+03 |\n",
+ "| Time | 653 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 28 |\n",
+ "| AverageEpRet | -965 |\n",
+ "| StdEpRet | 522 |\n",
+ "| MaxEpRet | -172 |\n",
+ "| MinEpRet | -2.3e+03 |\n",
+ "| AverageTestEpRet | -599 |\n",
+ "| StdTestEpRet | 398 |\n",
+ "| MaxTestEpRet | -163 |\n",
+ "| MinTestEpRet | -1.32e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.16e+04 |\n",
+ "| AverageQ1Vals | -154 |\n",
+ "| StdQ1Vals | 226 |\n",
+ "| MaxQ1Vals | 176 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -154 |\n",
+ "| StdQ2Vals | 226 |\n",
+ "| MaxQ2Vals | 188 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 973 |\n",
+ "| Time | 677 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 29 |\n",
+ "| AverageEpRet | -766 |\n",
+ "| StdEpRet | 367 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.69e+03 |\n",
+ "| AverageTestEpRet | -659 |\n",
+ "| StdTestEpRet | 229 |\n",
+ "| MaxTestEpRet | -303 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.38e+04 |\n",
+ "| AverageQ1Vals | -153 |\n",
+ "| StdQ1Vals | 225 |\n",
+ "| MaxQ1Vals | 173 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -153 |\n",
+ "| StdQ2Vals | 225 |\n",
+ "| MaxQ2Vals | 176 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 961 |\n",
+ "| Time | 702 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 30 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -388 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -686 |\n",
+ "| StdTestEpRet | 188 |\n",
+ "| MaxTestEpRet | -312 |\n",
+ "| MinTestEpRet | -977 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+04 |\n",
+ "| AverageQ1Vals | -151 |\n",
+ "| StdQ1Vals | 222 |\n",
+ "| MaxQ1Vals | 162 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -151 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 159 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 131 |\n",
+ "| LossQ | 913 |\n",
+ "| Time | 727 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 31 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 442 |\n",
+ "| MaxEpRet | -163 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -678 |\n",
+ "| StdTestEpRet | 371 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.82e+04 |\n",
+ "| AverageQ1Vals | -149 |\n",
+ "| StdQ1Vals | 219 |\n",
+ "| MaxQ1Vals | 149 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -149 |\n",
+ "| StdQ2Vals | 219 |\n",
+ "| MaxQ2Vals | 153 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 855 |\n",
+ "| Time | 753 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 32 |\n",
+ "| AverageEpRet | -760 |\n",
+ "| StdEpRet | 313 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -1.49e+03 |\n",
+ "| AverageTestEpRet | -759 |\n",
+ "| StdTestEpRet | 430 |\n",
+ "| MaxTestEpRet | -256 |\n",
+ "| MinTestEpRet | -1.64e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.04e+04 |\n",
+ "| AverageQ1Vals | -150 |\n",
+ "| StdQ1Vals | 217 |\n",
+ "| MaxQ1Vals | 146 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -150 |\n",
+ "| StdQ2Vals | 217 |\n",
+ "| MaxQ2Vals | 145 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 835 |\n",
+ "| Time | 775 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 33 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 395 |\n",
+ "| MaxEpRet | -57.1 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 368 |\n",
+ "| MaxTestEpRet | -120 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.26e+04 |\n",
+ "| AverageQ1Vals | -147 |\n",
+ "| StdQ1Vals | 213 |\n",
+ "| MaxQ1Vals | 130 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -147 |\n",
+ "| StdQ2Vals | 213 |\n",
+ "| MaxQ2Vals | 125 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 129 |\n",
+ "| LossQ | 800 |\n",
+ "| Time | 796 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 34 |\n",
+ "| AverageEpRet | -732 |\n",
+ "| StdEpRet | 361 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.6e+03 |\n",
+ "| AverageTestEpRet | -737 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -278 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.48e+04 |\n",
+ "| AverageQ1Vals | -144 |\n",
+ "| StdQ1Vals | 211 |\n",
+ "| MaxQ1Vals | 133 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -144 |\n",
+ "| StdQ2Vals | 211 |\n",
+ "| MaxQ2Vals | 121 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 126 |\n",
+ "| LossQ | 784 |\n",
+ "| Time | 820 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 35 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -159 |\n",
+ "| MinEpRet | -1.58e+03 |\n",
+ "| AverageTestEpRet | -766 |\n",
+ "| StdTestEpRet | 358 |\n",
+ "| MaxTestEpRet | -85.9 |\n",
+ "| MinTestEpRet | -1.35e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.7e+04 |\n",
+ "| AverageQ1Vals | -143 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -143 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 107 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 730 |\n",
+ "| Time | 846 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 36 |\n",
+ "| AverageEpRet | -848 |\n",
+ "| StdEpRet | 733 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -3.32e+03 |\n",
+ "| AverageTestEpRet | -765 |\n",
+ "| StdTestEpRet | 340 |\n",
+ "| MaxTestEpRet | -191 |\n",
+ "| MinTestEpRet | -1.48e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.92e+04 |\n",
+ "| AverageQ1Vals | -141 |\n",
+ "| StdQ1Vals | 207 |\n",
+ "| MaxQ1Vals | 115 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -141 |\n",
+ "| StdQ2Vals | 207 |\n",
+ "| MaxQ2Vals | 108 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 703 |\n",
+ "| Time | 873 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 37 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 379 |\n",
+ "| MaxEpRet | -11.3 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -646 |\n",
+ "| StdTestEpRet | 399 |\n",
+ "| MaxTestEpRet | -102 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.14e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 204 |\n",
+ "| MaxQ1Vals | 103 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 204 |\n",
+ "| MaxQ2Vals | 96.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 667 |\n",
+ "| Time | 900 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 38 |\n",
+ "| AverageEpRet | -634 |\n",
+ "| StdEpRet | 304 |\n",
+ "| MaxEpRet | -44.1 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -716 |\n",
+ "| StdTestEpRet | 356 |\n",
+ "| MaxTestEpRet | -263 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.36e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 203 |\n",
+ "| MaxQ1Vals | 91.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 203 |\n",
+ "| MaxQ2Vals | 86 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 664 |\n",
+ "| Time | 929 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 39 |\n",
+ "| AverageEpRet | -630 |\n",
+ "| StdEpRet | 375 |\n",
+ "| MaxEpRet | -127 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -591 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -204 |\n",
+ "| MinTestEpRet | -946 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.58e+04 |\n",
+ "| AverageQ1Vals | -139 |\n",
+ "| StdQ1Vals | 201 |\n",
+ "| MaxQ1Vals | 86.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -139 |\n",
+ "| StdQ2Vals | 201 |\n",
+ "| MaxQ2Vals | 77.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 649 |\n",
+ "| Time | 960 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 40 |\n",
+ "| AverageEpRet | -506 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -67.2 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -621 |\n",
+ "| StdTestEpRet | 351 |\n",
+ "| MaxTestEpRet | -171 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+04 |\n",
+ "| AverageQ1Vals | -137 |\n",
+ "| StdQ1Vals | 199 |\n",
+ "| MaxQ1Vals | 77.5 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -137 |\n",
+ "| StdQ2Vals | 199 |\n",
+ "| MaxQ2Vals | 66.2 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 122 |\n",
+ "| LossQ | 629 |\n",
+ "| Time | 991 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 41 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 301 |\n",
+ "| MaxEpRet | -182 |\n",
+ "| MinEpRet | -1.31e+03 |\n",
+ "| AverageTestEpRet | -784 |\n",
+ "| StdTestEpRet | 309 |\n",
+ "| MaxTestEpRet | -408 |\n",
+ "| MinTestEpRet | -1.4e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.02e+04 |\n",
+ "| AverageQ1Vals | -136 |\n",
+ "| StdQ1Vals | 197 |\n",
+ "| MaxQ1Vals | 74.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -136 |\n",
+ "| StdQ2Vals | 197 |\n",
+ "| MaxQ2Vals | 74.3 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 121 |\n",
+ "| LossQ | 587 |\n",
+ "| Time | 1.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 42 |\n",
+ "| AverageEpRet | -623 |\n",
+ "| StdEpRet | 295 |\n",
+ "| MaxEpRet | -69.3 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -806 |\n",
+ "| StdTestEpRet | 386 |\n",
+ "| MaxTestEpRet | -285 |\n",
+ "| MinTestEpRet | -1.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.24e+04 |\n",
+ "| AverageQ1Vals | -134 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | 59.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -134 |\n",
+ "| StdQ2Vals | 196 |\n",
+ "| MaxQ2Vals | 67.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 120 |\n",
+ "| LossQ | 584 |\n",
+ "| Time | 1.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 43 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 255 |\n",
+ "| MaxEpRet | -79.6 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -620 |\n",
+ "| StdTestEpRet | 300 |\n",
+ "| MaxTestEpRet | -160 |\n",
+ "| MinTestEpRet | -971 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.46e+04 |\n",
+ "| AverageQ1Vals | -133 |\n",
+ "| StdQ1Vals | 194 |\n",
+ "| MaxQ1Vals | 63.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -133 |\n",
+ "| StdQ2Vals | 194 |\n",
+ "| MaxQ2Vals | 68.2 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 561 |\n",
+ "| Time | 1.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 44 |\n",
+ "| AverageEpRet | -583 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -63.3 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -507 |\n",
+ "| StdTestEpRet | 296 |\n",
+ "| MaxTestEpRet | -107 |\n",
+ "| MinTestEpRet | -996 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.68e+04 |\n",
+ "| AverageQ1Vals | -132 |\n",
+ "| StdQ1Vals | 191 |\n",
+ "| MaxQ1Vals | 49.2 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -132 |\n",
+ "| StdQ2Vals | 191 |\n",
+ "| MaxQ2Vals | 49.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 532 |\n",
+ "| Time | 1.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 45 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 309 |\n",
+ "| MaxEpRet | -147 |\n",
+ "| MinEpRet | -1.23e+03 |\n",
+ "| AverageTestEpRet | -727 |\n",
+ "| StdTestEpRet | 348 |\n",
+ "| MaxTestEpRet | -203 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.9e+04 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 50.6 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 47.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 116 |\n",
+ "| LossQ | 517 |\n",
+ "| Time | 1.14e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 46 |\n",
+ "| AverageEpRet | -649 |\n",
+ "| StdEpRet | 325 |\n",
+ "| MaxEpRet | -151 |\n",
+ "| MinEpRet | -1.5e+03 |\n",
+ "| AverageTestEpRet | -647 |\n",
+ "| StdTestEpRet | 317 |\n",
+ "| MaxTestEpRet | -219 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.01e+05 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 90.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 115 |\n",
+ "| LossQ | 514 |\n",
+ "| Time | 1.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 47 |\n",
+ "| AverageEpRet | -444 |\n",
+ "| StdEpRet | 183 |\n",
+ "| MaxEpRet | -183 |\n",
+ "| MinEpRet | -968 |\n",
+ "| AverageTestEpRet | -669 |\n",
+ "| StdTestEpRet | 245 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.12e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.03e+05 |\n",
+ "| AverageQ1Vals | -127 |\n",
+ "| StdQ1Vals | 185 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -127 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | 55.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 113 |\n",
+ "| LossQ | 489 |\n",
+ "| Time | 1.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 48 |\n",
+ "| AverageEpRet | -673 |\n",
+ "| StdEpRet | 332 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.41e+03 |\n",
+ "| AverageTestEpRet | -534 |\n",
+ "| StdTestEpRet | 252 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.06e+05 |\n",
+ "| AverageQ1Vals | -125 |\n",
+ "| StdQ1Vals | 182 |\n",
+ "| MaxQ1Vals | 96.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -125 |\n",
+ "| StdQ2Vals | 182 |\n",
+ "| MaxQ2Vals | 93.5 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 473 |\n",
+ "| Time | 1.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 49 |\n",
+ "| AverageEpRet | -475 |\n",
+ "| StdEpRet | 243 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -572 |\n",
+ "| StdTestEpRet | 339 |\n",
+ "| MaxTestEpRet | -155 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.08e+05 |\n",
+ "| AverageQ1Vals | -124 |\n",
+ "| StdQ1Vals | 181 |\n",
+ "| MaxQ1Vals | 66.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -124 |\n",
+ "| StdQ2Vals | 181 |\n",
+ "| MaxQ2Vals | 60.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 466 |\n",
+ "| Time | 1.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 50 |\n",
+ "| AverageEpRet | -581 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -121 |\n",
+ "| MinEpRet | -1.34e+03 |\n",
+ "| AverageTestEpRet | -687 |\n",
+ "| StdTestEpRet | 246 |\n",
+ "| MaxTestEpRet | -246 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 50.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 439 |\n",
+ "| Time | 1.31e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 51 |\n",
+ "| AverageEpRet | -599 |\n",
+ "| StdEpRet | 287 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 204 |\n",
+ "| MaxTestEpRet | -216 |\n",
+ "| MinTestEpRet | -856 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.12e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 78.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 76.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 110 |\n",
+ "| LossQ | 437 |\n",
+ "| Time | 1.34e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 52 |\n",
+ "| AverageEpRet | -636 |\n",
+ "| StdEpRet | 283 |\n",
+ "| MaxEpRet | -220 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -712 |\n",
+ "| StdTestEpRet | 185 |\n",
+ "| MaxTestEpRet | -457 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.14e+05 |\n",
+ "| AverageQ1Vals | -121 |\n",
+ "| StdQ1Vals | 178 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -121 |\n",
+ "| StdQ2Vals | 178 |\n",
+ "| MaxQ2Vals | 54.7 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 431 |\n",
+ "| Time | 1.37e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 53 |\n",
+ "| AverageEpRet | -591 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -132 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -551 |\n",
+ "| StdTestEpRet | 206 |\n",
+ "| MaxTestEpRet | -333 |\n",
+ "| MinTestEpRet | -975 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.17e+05 |\n",
+ "| AverageQ1Vals | -119 |\n",
+ "| StdQ1Vals | 176 |\n",
+ "| MaxQ1Vals | 54.4 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -119 |\n",
+ "| StdQ2Vals | 176 |\n",
+ "| MaxQ2Vals | 89.1 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 108 |\n",
+ "| LossQ | 418 |\n",
+ "| Time | 1.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 54 |\n",
+ "| AverageEpRet | -426 |\n",
+ "| StdEpRet | 230 |\n",
+ "| MaxEpRet | -90.2 |\n",
+ "| MinEpRet | -906 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 197 |\n",
+ "| MaxTestEpRet | -124 |\n",
+ "| MinTestEpRet | -708 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.19e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 175 |\n",
+ "| MaxQ1Vals | 47.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 175 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 401 |\n",
+ "| Time | 1.43e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 55 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -939 |\n",
+ "| AverageTestEpRet | -615 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -400 |\n",
+ "| MinTestEpRet | -952 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.21e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 174 |\n",
+ "| MaxQ1Vals | 54.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 174 |\n",
+ "| MaxQ2Vals | 68.6 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 392 |\n",
+ "| Time | 1.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 56 |\n",
+ "| AverageEpRet | -552 |\n",
+ "| StdEpRet | 234 |\n",
+ "| MaxEpRet | -228 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -400 |\n",
+ "| StdTestEpRet | 293 |\n",
+ "| MaxTestEpRet | -66.3 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.23e+05 |\n",
+ "| AverageQ1Vals | -117 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | 66.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -117 |\n",
+ "| StdQ2Vals | 173 |\n",
+ "| MaxQ2Vals | 69.5 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 105 |\n",
+ "| LossQ | 400 |\n",
+ "| Time | 1.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 57 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 318 |\n",
+ "| MaxEpRet | -33.7 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -480 |\n",
+ "| StdTestEpRet | 210 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -802 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.25e+05 |\n",
+ "| AverageQ1Vals | -114 |\n",
+ "| StdQ1Vals | 171 |\n",
+ "| MaxQ1Vals | 68.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -114 |\n",
+ "| StdQ2Vals | 171 |\n",
+ "| MaxQ2Vals | 68.8 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 103 |\n",
+ "| LossQ | 380 |\n",
+ "| Time | 1.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 58 |\n",
+ "| AverageEpRet | -590 |\n",
+ "| StdEpRet | 261 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 234 |\n",
+ "| MaxTestEpRet | -206 |\n",
+ "| MinTestEpRet | -935 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.28e+05 |\n",
+ "| AverageQ1Vals | -113 |\n",
+ "| StdQ1Vals | 170 |\n",
+ "| MaxQ1Vals | 72.9 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -113 |\n",
+ "| StdQ2Vals | 170 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 368 |\n",
+ "| Time | 1.56e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 59 |\n",
+ "| AverageEpRet | -588 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -1.05e+03 |\n",
+ "| AverageTestEpRet | -467 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -763 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.3e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 169 |\n",
+ "| MaxQ1Vals | 64.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 169 |\n",
+ "| MaxQ2Vals | 93.3 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 367 |\n",
+ "| Time | 1.6e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 60 |\n",
+ "| AverageEpRet | -495 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -44.8 |\n",
+ "| MinEpRet | -1.71e+03 |\n",
+ "| AverageTestEpRet | -654 |\n",
+ "| StdTestEpRet | 261 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.05e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 168 |\n",
+ "| MaxQ1Vals | 68.9 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 168 |\n",
+ "| MaxQ2Vals | 73.7 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 101 |\n",
+ "| LossQ | 363 |\n",
+ "| Time | 1.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 61 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 376 |\n",
+ "| MaxEpRet | -74.9 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -574 |\n",
+ "| StdTestEpRet | 410 |\n",
+ "| MaxTestEpRet | -144 |\n",
+ "| MinTestEpRet | -1.57e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.34e+05 |\n",
+ "| AverageQ1Vals | -110 |\n",
+ "| StdQ1Vals | 166 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -110 |\n",
+ "| StdQ2Vals | 166 |\n",
+ "| MaxQ2Vals | 52.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 100 |\n",
+ "| LossQ | 349 |\n",
+ "| Time | 1.66e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 62 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -485 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -886 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.36e+05 |\n",
+ "| AverageQ1Vals | -109 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 67.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -109 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 98.7 |\n",
+ "| LossQ | 358 |\n",
+ "| Time | 1.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 63 |\n",
+ "| AverageEpRet | -448 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -56.4 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -341 |\n",
+ "| StdTestEpRet | 181 |\n",
+ "| MaxTestEpRet | -63.6 |\n",
+ "| MinTestEpRet | -608 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.39e+05 |\n",
+ "| AverageQ1Vals | -108 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -108 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 52.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 97.8 |\n",
+ "| LossQ | 344 |\n",
+ "| Time | 1.73e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 64 |\n",
+ "| AverageEpRet | -525 |\n",
+ "| StdEpRet | 226 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -532 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -302 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.41e+05 |\n",
+ "| AverageQ1Vals | -106 |\n",
+ "| StdQ1Vals | 164 |\n",
+ "| MaxQ1Vals | 50.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -106 |\n",
+ "| StdQ2Vals | 164 |\n",
+ "| MaxQ2Vals | 37.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 95.7 |\n",
+ "| LossQ | 340 |\n",
+ "| Time | 1.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 65 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -31.2 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -520 |\n",
+ "| StdTestEpRet | 359 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -1.51e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.43e+05 |\n",
+ "| AverageQ1Vals | -105 |\n",
+ "| StdQ1Vals | 162 |\n",
+ "| MaxQ1Vals | 58.3 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -105 |\n",
+ "| StdQ2Vals | 162 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 95.2 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 66 |\n",
+ "| AverageEpRet | -622 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -126 |\n",
+ "| MinEpRet | -1.33e+03 |\n",
+ "| AverageTestEpRet | -525 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.45e+05 |\n",
+ "| AverageQ1Vals | -104 |\n",
+ "| StdQ1Vals | 163 |\n",
+ "| MaxQ1Vals | 46.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -104 |\n",
+ "| StdQ2Vals | 163 |\n",
+ "| MaxQ2Vals | 73.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 94.4 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.83e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 67 |\n",
+ "| AverageEpRet | -483 |\n",
+ "| StdEpRet | 204 |\n",
+ "| MaxEpRet | -118 |\n",
+ "| MinEpRet | -836 |\n",
+ "| AverageTestEpRet | -530 |\n",
+ "| StdTestEpRet | 231 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -994 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.47e+05 |\n",
+ "| AverageQ1Vals | -103 |\n",
+ "| StdQ1Vals | 161 |\n",
+ "| MaxQ1Vals | 56 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -103 |\n",
+ "| StdQ2Vals | 161 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 93.2 |\n",
+ "| LossQ | 325 |\n",
+ "| Time | 1.86e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 68 |\n",
+ "| AverageEpRet | -443 |\n",
+ "| StdEpRet | 266 |\n",
+ "| MaxEpRet | -17.7 |\n",
+ "| MinEpRet | -926 |\n",
+ "| AverageTestEpRet | -498 |\n",
+ "| StdTestEpRet | 213 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -779 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.5e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 71.7 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 76.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.3 |\n",
+ "| LossQ | 311 |\n",
+ "| Time | 1.89e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 69 |\n",
+ "| AverageEpRet | -542 |\n",
+ "| StdEpRet | 422 |\n",
+ "| MaxEpRet | -74.6 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 205 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -905 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.52e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 57.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 106 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.1 |\n",
+ "| LossQ | 315 |\n",
+ "| Time | 1.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 70 |\n",
+ "| AverageEpRet | -556 |\n",
+ "| StdEpRet | 188 |\n",
+ "| MaxEpRet | -166 |\n",
+ "| MinEpRet | -827 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -247 |\n",
+ "| MinTestEpRet | -782 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 104 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 89.6 |\n",
+ "| LossQ | 298 |\n",
+ "| Time | 1.96e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 71 |\n",
+ "| AverageEpRet | -508 |\n",
+ "| StdEpRet | 360 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.65e+03 |\n",
+ "| AverageTestEpRet | -503 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -98.4 |\n",
+ "| MinTestEpRet | -933 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.56e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 68.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 62.4 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.9 |\n",
+ "| LossQ | 310 |\n",
+ "| Time | 1.98e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 72 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -154 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 299 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.58e+05 |\n",
+ "| AverageQ1Vals | -98.4 |\n",
+ "| StdQ1Vals | 157 |\n",
+ "| MaxQ1Vals | 40.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -98.4 |\n",
+ "| StdQ2Vals | 157 |\n",
+ "| MaxQ2Vals | 65.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 307 |\n",
+ "| Time | 2.01e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 73 |\n",
+ "| AverageEpRet | -531 |\n",
+ "| StdEpRet | 253 |\n",
+ "| MaxEpRet | -152 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -403 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -66.2 |\n",
+ "| MinTestEpRet | -865 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.61e+05 |\n",
+ "| AverageQ1Vals | -98 |\n",
+ "| StdQ1Vals | 156 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -98 |\n",
+ "| StdQ2Vals | 156 |\n",
+ "| MaxQ2Vals | 61.2 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 290 |\n",
+ "| Time | 2.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 74 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 225 |\n",
+ "| MaxEpRet | -242 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -421 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -972 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.63e+05 |\n",
+ "| AverageQ1Vals | -96.8 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 84.5 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -96.8 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 44 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 87.6 |\n",
+ "| LossQ | 280 |\n",
+ "| Time | 2.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 75 |\n",
+ "| AverageEpRet | -438 |\n",
+ "| StdEpRet | 207 |\n",
+ "| MaxEpRet | -103 |\n",
+ "| MinEpRet | -930 |\n",
+ "| AverageTestEpRet | -416 |\n",
+ "| StdTestEpRet | 241 |\n",
+ "| MaxTestEpRet | -135 |\n",
+ "| MinTestEpRet | -887 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.65e+05 |\n",
+ "| AverageQ1Vals | -96.7 |\n",
+ "| StdQ1Vals | 155 |\n",
+ "| MaxQ1Vals | 72.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -96.7 |\n",
+ "| StdQ2Vals | 155 |\n",
+ "| MaxQ2Vals | 75.7 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 88.1 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 76 |\n",
+ "| AverageEpRet | -639 |\n",
+ "| StdEpRet | 315 |\n",
+ "| MaxEpRet | -214 |\n",
+ "| MinEpRet | -1.51e+03 |\n",
+ "| AverageTestEpRet | -437 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -19.2 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.67e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 48.5 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 46.2 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 86.8 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.14e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 77 |\n",
+ "| AverageEpRet | -468 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -901 |\n",
+ "| AverageTestEpRet | -556 |\n",
+ "| StdTestEpRet | 262 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -812 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.69e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 37.2 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 49.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.9 |\n",
+ "| LossQ | 283 |\n",
+ "| Time | 2.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 78 |\n",
+ "| AverageEpRet | -568 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -123 |\n",
+ "| MinEpRet | -1.27e+03 |\n",
+ "| AverageTestEpRet | -550 |\n",
+ "| StdTestEpRet | 360 |\n",
+ "| MaxTestEpRet | -164 |\n",
+ "| MinTestEpRet | -1.26e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.72e+05 |\n",
+ "| AverageQ1Vals | -94.5 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 47.8 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -94.5 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 92 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 85.9 |\n",
+ "| LossQ | 265 |\n",
+ "| Time | 2.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 79 |\n",
+ "| AverageEpRet | -566 |\n",
+ "| StdEpRet | 337 |\n",
+ "| MaxEpRet | -96.5 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -543 |\n",
+ "| StdTestEpRet | 365 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.74e+05 |\n",
+ "| AverageQ1Vals | -94.8 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 69.7 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -94.8 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 75.6 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 278 |\n",
+ "| Time | 2.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 80 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 290 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 196 |\n",
+ "| MaxTestEpRet | -182 |\n",
+ "| MinTestEpRet | -783 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+05 |\n",
+ "| AverageQ1Vals | -94.6 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 59.6 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -94.6 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 74.6 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 271 |\n",
+ "| Time | 2.28e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 81 |\n",
+ "| AverageEpRet | -535 |\n",
+ "| StdEpRet | 217 |\n",
+ "| MaxEpRet | -131 |\n",
+ "| MinEpRet | -999 |\n",
+ "| AverageTestEpRet | -422 |\n",
+ "| StdTestEpRet | 270 |\n",
+ "| MaxTestEpRet | -67.3 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.78e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 150 |\n",
+ "| MaxQ1Vals | 47.4 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 150 |\n",
+ "| MaxQ2Vals | 71 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 85.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.31e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 82 |\n",
+ "| AverageEpRet | -505 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -79.9 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -734 |\n",
+ "| StdTestEpRet | 289 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.22e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.8e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 149 |\n",
+ "| MaxQ1Vals | 53 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 149 |\n",
+ "| MaxQ2Vals | 67.3 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 86.6 |\n",
+ "| LossQ | 267 |\n",
+ "| Time | 2.35e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 83 |\n",
+ "| AverageEpRet | -585 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -96.7 |\n",
+ "| MinEpRet | -1.3e+03 |\n",
+ "| AverageTestEpRet | -436 |\n",
+ "| StdTestEpRet | 169 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -748 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.83e+05 |\n",
+ "| AverageQ1Vals | -92.9 |\n",
+ "| StdQ1Vals | 147 |\n",
+ "| MaxQ1Vals | 44.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -92.9 |\n",
+ "| StdQ2Vals | 147 |\n",
+ "| MaxQ2Vals | 75.2 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 84.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.38e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 84 |\n",
+ "| AverageEpRet | -406 |\n",
+ "| StdEpRet | 232 |\n",
+ "| MaxEpRet | -128 |\n",
+ "| MinEpRet | -924 |\n",
+ "| AverageTestEpRet | -650 |\n",
+ "| StdTestEpRet | 236 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.85e+05 |\n",
+ "| AverageQ1Vals | -92.5 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 46.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -92.5 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 80.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 84.4 |\n",
+ "| LossQ | 258 |\n",
+ "| Time | 2.41e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 85 |\n",
+ "| AverageEpRet | -474 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -180 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -527 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -166 |\n",
+ "| MinTestEpRet | -737 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.87e+05 |\n",
+ "| AverageQ1Vals | -91.8 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 51.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -91.8 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 33.2 |\n",
+ "| MinQ2Vals | -1.06e+03 |\n",
+ "| LossPi | 84.1 |\n",
+ "| LossQ | 249 |\n",
+ "| Time | 2.44e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 86 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 264 |\n",
+ "| MaxEpRet | -112 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -665 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.89e+05 |\n",
+ "| AverageQ1Vals | -91.6 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 65.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -91.7 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 67.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 83.5 |\n",
+ "| LossQ | 238 |\n",
+ "| Time | 2.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 87 |\n",
+ "| AverageEpRet | -515 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -122 |\n",
+ "| MinEpRet | -976 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 202 |\n",
+ "| MaxTestEpRet | -180 |\n",
+ "| MinTestEpRet | -829 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.91e+05 |\n",
+ "| AverageQ1Vals | -92.1 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -92.1 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 54.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 83.6 |\n",
+ "| LossQ | 256 |\n",
+ "| Time | 2.51e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 88 |\n",
+ "| AverageEpRet | -572 |\n",
+ "| StdEpRet | 258 |\n",
+ "| MaxEpRet | -57 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -508 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -217 |\n",
+ "| MinTestEpRet | -794 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.94e+05 |\n",
+ "| AverageQ1Vals | -91.3 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 42.6 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -91.3 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 58 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 83.7 |\n",
+ "| LossQ | 246 |\n",
+ "| Time | 2.54e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 89 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 241 |\n",
+ "| MaxEpRet | -49.9 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -562 |\n",
+ "| StdTestEpRet | 306 |\n",
+ "| MaxTestEpRet | -68.2 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.96e+05 |\n",
+ "| AverageQ1Vals | -90.3 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -90.3 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 54.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 83 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.57e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 90 |\n",
+ "| AverageEpRet | -478 |\n",
+ "| StdEpRet | 286 |\n",
+ "| MaxEpRet | -68 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -603 |\n",
+ "| StdTestEpRet | 390 |\n",
+ "| MaxTestEpRet | -123 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.6 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.6e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 91 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 252 |\n",
+ "| MaxEpRet | -101 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -566 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -62.9 |\n",
+ "| MinTestEpRet | -1.02e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 25.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.7 |\n",
+ "| LossQ | 241 |\n",
+ "| Time | 2.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 92 |\n",
+ "| AverageEpRet | -425 |\n",
+ "| StdEpRet | 205 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -998 |\n",
+ "| AverageTestEpRet | -536 |\n",
+ "| StdTestEpRet | 271 |\n",
+ "| MaxTestEpRet | -209 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.02e+05 |\n",
+ "| AverageQ1Vals | -88.7 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -88.7 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 32.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 81.3 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.67e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 93 |\n",
+ "| AverageEpRet | -498 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -83.8 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -523 |\n",
+ "| StdTestEpRet | 286 |\n",
+ "| MaxTestEpRet | -83.5 |\n",
+ "| MinTestEpRet | -967 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.05e+05 |\n",
+ "| AverageQ1Vals | -88.2 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 97.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -88.2 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 36.8 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 80.8 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 94 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 231 |\n",
+ "| MaxEpRet | -88.9 |\n",
+ "| MinEpRet | -946 |\n",
+ "| AverageTestEpRet | -407 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -793 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.07e+05 |\n",
+ "| AverageQ1Vals | -87.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -87.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.6 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 80.4 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.73e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 95 |\n",
+ "| AverageEpRet | -471 |\n",
+ "| StdEpRet | 257 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -994 |\n",
+ "| AverageTestEpRet | -563 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -827 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.09e+05 |\n",
+ "| AverageQ1Vals | -87.2 |\n",
+ "| StdQ1Vals | 141 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -87.2 |\n",
+ "| StdQ2Vals | 141 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 80.3 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 96 |\n",
+ "| AverageEpRet | -473 |\n",
+ "| StdEpRet | 268 |\n",
+ "| MaxEpRet | -76.1 |\n",
+ "| MinEpRet | -1.03e+03 |\n",
+ "| AverageTestEpRet | -392 |\n",
+ "| StdTestEpRet | 191 |\n",
+ "| MaxTestEpRet | -90.2 |\n",
+ "| MinTestEpRet | -713 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.11e+05 |\n",
+ "| AverageQ1Vals | -86.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 32.9 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -86.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 40.5 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 79.5 |\n",
+ "| LossQ | 233 |\n",
+ "| Time | 2.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 97 |\n",
+ "| AverageEpRet | -497 |\n",
+ "| StdEpRet | 345 |\n",
+ "| MaxEpRet | -37.1 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 227 |\n",
+ "| MaxTestEpRet | -72.6 |\n",
+ "| MinTestEpRet | -742 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.13e+05 |\n",
+ "| AverageQ1Vals | -86.5 |\n",
+ "| StdQ1Vals | 140 |\n",
+ "| MaxQ1Vals | 43 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -86.5 |\n",
+ "| StdQ2Vals | 140 |\n",
+ "| MaxQ2Vals | 38.9 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 79.6 |\n",
+ "| LossQ | 234 |\n",
+ "| Time | 2.82e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 98 |\n",
+ "| AverageEpRet | -429 |\n",
+ "| StdEpRet | 215 |\n",
+ "| MaxEpRet | -135 |\n",
+ "| MinEpRet | -848 |\n",
+ "| AverageTestEpRet | -511 |\n",
+ "| StdTestEpRet | 337 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -1.37e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.16e+05 |\n",
+ "| AverageQ1Vals | -86.1 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -86.1 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43.7 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 79.1 |\n",
+ "| LossQ | 226 |\n",
+ "| Time | 2.85e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 99 |\n",
+ "| AverageEpRet | -514 |\n",
+ "| StdEpRet | 278 |\n",
+ "| MaxEpRet | -141 |\n",
+ "| MinEpRet | -1.21e+03 |\n",
+ "| AverageTestEpRet | -633 |\n",
+ "| StdTestEpRet | 489 |\n",
+ "| MaxTestEpRet | -181 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.18e+05 |\n",
+ "| AverageQ1Vals | -84.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -84.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 27.5 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 77.7 |\n",
+ "| LossQ | 225 |\n",
+ "| Time | 2.88e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 100 |\n",
+ "| AverageEpRet | -557 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -26 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -544 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -1.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+05 |\n",
+ "| AverageQ1Vals | -84 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 51.4 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -84 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43 |\n",
+ "| MinQ2Vals | -1.22e+03 |\n",
+ "| LossPi | 76.6 |\n",
+ "| LossQ | 220 |\n",
+ "| Time | 2.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 101 |\n",
+ "| AverageEpRet | -434 |\n",
+ "| StdEpRet | 192 |\n",
+ "| MaxEpRet | -155 |\n",
+ "| MinEpRet | -806 |\n",
+ "| AverageTestEpRet | -418 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -244 |\n",
+ "| MinTestEpRet | -730 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.22e+05 |\n",
+ "| AverageQ1Vals | -82.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 35.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -82.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 54.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 76.3 |\n",
+ "| LossQ | 219 |\n",
+ "| Time | 2.95e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 102 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -120 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -490 |\n",
+ "| StdTestEpRet | 223 |\n",
+ "| MaxTestEpRet | -73.4 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.24e+05 |\n",
+ "| AverageQ1Vals | -81.2 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 96.8 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -81.2 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 71.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 74.2 |\n",
+ "| LossQ | 213 |\n",
+ "| Time | 2.99e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 103 |\n",
+ "| AverageEpRet | -617 |\n",
+ "| StdEpRet | 327 |\n",
+ "| MaxEpRet | -104 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 214 |\n",
+ "| MaxTestEpRet | -248 |\n",
+ "| MinTestEpRet | -906 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.27e+05 |\n",
+ "| AverageQ1Vals | -80.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 70.5 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -80.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 41 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 74.1 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 104 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -65.7 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -425 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -774 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.29e+05 |\n",
+ "| AverageQ1Vals | -79.9 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 41.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -79.9 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 44.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 73.2 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 105 |\n",
+ "| AverageEpRet | -469 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -144 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -317 |\n",
+ "| StdTestEpRet | 137 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -541 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.31e+05 |\n",
+ "| AverageQ1Vals | -78.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 24.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -78.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 38.7 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.6 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 106 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 244 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -989 |\n",
+ "| AverageTestEpRet | -542 |\n",
+ "| StdTestEpRet | 277 |\n",
+ "| MaxTestEpRet | -129 |\n",
+ "| MinTestEpRet | -965 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.33e+05 |\n",
+ "| AverageQ1Vals | -79.1 |\n",
+ "| StdQ1Vals | 136 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -79.1 |\n",
+ "| StdQ2Vals | 136 |\n",
+ "| MaxQ2Vals | 54 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 72.7 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 107 |\n",
+ "| AverageEpRet | -571 |\n",
+ "| StdEpRet | 245 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -538 |\n",
+ "| StdTestEpRet | 189 |\n",
+ "| MaxTestEpRet | -255 |\n",
+ "| MinTestEpRet | -880 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.35e+05 |\n",
+ "| AverageQ1Vals | -78.4 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 33.8 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -78.4 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 71.6 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.15e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 108 |\n",
+ "| AverageEpRet | -481 |\n",
+ "| StdEpRet | 187 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -837 |\n",
+ "| AverageTestEpRet | -399 |\n",
+ "| StdTestEpRet | 162 |\n",
+ "| MaxTestEpRet | -149 |\n",
+ "| MinTestEpRet | -668 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.38e+05 |\n",
+ "| AverageQ1Vals | -78 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 34 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -78 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.1 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.18e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 109 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 272 |\n",
+ "| MaxEpRet | -63.6 |\n",
+ "| MinEpRet | -1.04e+03 |\n",
+ "| AverageTestEpRet | -531 |\n",
+ "| StdTestEpRet | 326 |\n",
+ "| MaxTestEpRet | -90.1 |\n",
+ "| MinTestEpRet | -1.19e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.4e+05 |\n",
+ "| AverageQ1Vals | -76.9 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 59 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.9 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 42.2 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.7 |\n",
+ "| LossQ | 212 |\n",
+ "| Time | 3.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 110 |\n",
+ "| AverageEpRet | -523 |\n",
+ "| StdEpRet | 321 |\n",
+ "| MaxEpRet | -29.2 |\n",
+ "| MinEpRet | -1.39e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 282 |\n",
+ "| MaxTestEpRet | -187 |\n",
+ "| MinTestEpRet | -1.03e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+05 |\n",
+ "| AverageQ1Vals | -76.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 43.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 53.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 70.9 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 111 |\n",
+ "| AverageEpRet | -453 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -81.5 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -524 |\n",
+ "| StdTestEpRet | 105 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -689 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.44e+05 |\n",
+ "| AverageQ1Vals | -76.5 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 30.1 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.5 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.8 |\n",
+ "| MinQ2Vals | -1.23e+03 |\n",
+ "| LossPi | 70.4 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 112 |\n",
+ "| AverageEpRet | -596 |\n",
+ "| StdEpRet | 262 |\n",
+ "| MaxEpRet | -173 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -415 |\n",
+ "| StdTestEpRet | 161 |\n",
+ "| MaxTestEpRet | -137 |\n",
+ "| MinTestEpRet | -652 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.46e+05 |\n",
+ "| AverageQ1Vals | -76.3 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 34.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.3 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 46.7 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.3 |\n",
+ "| LossQ | 198 |\n",
+ "| Time | 3.3e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 113 |\n",
+ "| AverageEpRet | -482 |\n",
+ "| StdEpRet | 333 |\n",
+ "| MaxEpRet | -62.8 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -559 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -142 |\n",
+ "| MinTestEpRet | -1.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.49e+05 |\n",
+ "| AverageQ1Vals | -76.2 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 38.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -76.2 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 50.3 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.33e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 114 |\n",
+ "| AverageEpRet | -450 |\n",
+ "| StdEpRet | 216 |\n",
+ "| MaxEpRet | -94.9 |\n",
+ "| MinEpRet | -872 |\n",
+ "| AverageTestEpRet | -432 |\n",
+ "| StdTestEpRet | 132 |\n",
+ "| MaxTestEpRet | -232 |\n",
+ "| MinTestEpRet | -645 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.51e+05 |\n",
+ "| AverageQ1Vals | -75.7 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 44.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -75.7 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 69.6 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.36e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 115 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 240 |\n",
+ "| MaxEpRet | -108 |\n",
+ "| MinEpRet | -932 |\n",
+ "| AverageTestEpRet | -371 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -143 |\n",
+ "| MinTestEpRet | -616 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.53e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 21.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 29.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 205 |\n",
+ "| Time | 3.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 116 |\n",
+ "| AverageEpRet | -538 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -960 |\n",
+ "| AverageTestEpRet | -475 |\n",
+ "| StdTestEpRet | 311 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -1.23e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.55e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 26 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 37.1 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 69.3 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.43e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 117 |\n",
+ "| AverageEpRet | -437 |\n",
+ "| StdEpRet | 203 |\n",
+ "| MaxEpRet | -92.8 |\n",
+ "| MinEpRet | -857 |\n",
+ "| AverageTestEpRet | -582 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -79.8 |\n",
+ "| MinTestEpRet | -861 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.57e+05 |\n",
+ "| AverageQ1Vals | -75.5 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 49.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -75.5 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 51 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 69.4 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.47e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 118 |\n",
+ "| AverageEpRet | -549 |\n",
+ "| StdEpRet | 236 |\n",
+ "| MaxEpRet | -111 |\n",
+ "| MinEpRet | -1.28e+03 |\n",
+ "| AverageTestEpRet | -588 |\n",
+ "| StdTestEpRet | 404 |\n",
+ "| MaxTestEpRet | -138 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.6e+05 |\n",
+ "| AverageQ1Vals | -75.1 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 31.7 |\n",
+ "| MinQ1Vals | -1.05e+03 |\n",
+ "| AverageQ2Vals | -75.1 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 38.6 |\n",
+ "| MinQ2Vals | -1.05e+03 |\n",
+ "| LossPi | 69 |\n",
+ "| LossQ | 192 |\n",
+ "| Time | 3.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 119 |\n",
+ "| AverageEpRet | -491 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -102 |\n",
+ "| MinEpRet | -945 |\n",
+ "| AverageTestEpRet | -535 |\n",
+ "| StdTestEpRet | 217 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -879 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.62e+05 |\n",
+ "| AverageQ1Vals | -74.9 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 60.9 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.9 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 28.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 69.2 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 120 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 249 |\n",
+ "| MaxEpRet | -64.6 |\n",
+ "| MinEpRet | -885 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 242 |\n",
+ "| MaxTestEpRet | -56.1 |\n",
+ "| MinTestEpRet | -838 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+05 |\n",
+ "| AverageQ1Vals | -74.8 |\n",
+ "| StdQ1Vals | 129 |\n",
+ "| MaxQ1Vals | 105 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.8 |\n",
+ "| StdQ2Vals | 129 |\n",
+ "| MaxQ2Vals | 77.4 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 69.1 |\n",
+ "| LossQ | 197 |\n",
+ "| Time | 3.56e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Setup experiment\n",
+ "logger_kwargs = dict(output_dir='model', exp_name='v0')\n",
+ "\n",
+ "# Run training\n",
+ "spinup.td3_pytorch(GyroscopeEnv, seed=0, steps_per_epoch=110*20, epochs=120, replay_size=1000000, gamma=0.95,\n",
+ "polyak=0.995, batch_size=100, start_steps=20000, update_after=800, update_every=50,max_ep_len=110,logger_kwargs=logger_kwargs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup experiment\n",
+ "logger_kwargs = dict(output_dir='model', exp_name='v0')\n",
+ "\n",
+ "# Run training\n",
+ "spinup.td3_pytorch(GyroscopeEnv, seed=0, steps_per_epoch=110*20, epochs=120, replay_size=1000000, gamma=0.95,\n",
+ "polyak=0.995, batch_size=100, start_steps=20000, update_after=800, update_every=50,max_ep_len=110,logger_kwargs=logger_kwargs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Gn3Gp40bcOVz"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 972,
+ "status": "ok",
+ "timestamp": 1584036455886,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "6GyY0wE-QBOj",
+ "outputId": "9a5e9011-e024-4a65-8383-83c062cdcad9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-900.18321242575\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "env = GyroscopeEnv()\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = torch.load('model/pyt_save/model.pt')\n",
+ "\n",
+ "# Test parameters\n",
+ "x1,x2,x3,x4,x1_ref,x3_ref,w = 0,1,0,1,1,3,25\n",
+ "state = env.reset(np.array([x1,x2,x3,x4,x1_ref,x3_ref,w]))\n",
+ "val = []\n",
+ "act = []\n",
+ "dt = 0.01\n",
+ "time = np.arange(0, 4, dt)\n",
+ "score = 0\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " action = agent.act(torch.as_tensor(state, dtype=torch.float32))\n",
+ " act.append(action)\n",
+ " state, reward, done, _ = env.step(action)\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ "\n",
+ "env.close()\n",
+ "print(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "vvMjuRHDcfrE"
+ },
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 1856,
+ "status": "ok",
+ "timestamp": 1584036457424,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "aCZCqujgcMVA",
+ "outputId": "05490294-aab8-4933-ca9b-e13ba85ecf6d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2160x2160 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(4,2,figsize=(30,30))\n",
+ "plt.subplot(4,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "plt.plot(time, [row[4] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,2)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "plt.plot(time, [row[5] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,3)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[0]- row[4]) for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[2]- row[5]) for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,7)\n",
+ "plt.title('Red gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u1 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in act],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,8)\n",
+ "plt.title('Blue gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u2 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in act],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fz9r-gaStzpk"
+ },
+ "source": [
+ "## 3D rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "HQM1E8JYc-cC"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 400\n",
+ "\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "gyroscope_ddpg_testing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/training_spinuplib/.ipynb_checkpoints/gyroscope_paramsearch_spinuplib-checkpoint.ipynb b/code/training_spinuplib/.ipynb_checkpoints/gyroscope_paramsearch_spinuplib-checkpoint.ipynb
new file mode 100644
index 0000000..79898bd
--- /dev/null
+++ b/code/training_spinuplib/.ipynb_checkpoints/gyroscope_paramsearch_spinuplib-checkpoint.ipynb
@@ -0,0 +1,3786 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "x83dMPapQBN6"
+ },
+ "source": [
+ "# Gyroscope TD3 hyperparameter search (spinup library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8inmkEG5QHqx"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 655,
+ "status": "ok",
+ "timestamp": 1584030129316,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "DnnJGcnsQvZC",
+ "outputId": "9d6d1408-da29-44ed-af74-4ebdc2bd865a"
+ },
+ "outputs": [],
+ "source": [
+ "cd drive/My\\ Drive/Colab Notebooks/DDPG_uda_v0/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "fuJhdd479TpP"
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name '_C' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-4-0ec252ad867d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msolve_ivp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeque\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m __all__ += [name for name in dir(_C)\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'_'\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m not name.endswith('Base')]\n",
+ "\u001b[0;31mNameError\u001b[0m: name '_C' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from collections import deque\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "from vpython import *\n",
+ "import argparse\n",
+ "\n",
+ "import spinup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "O0O0t5ZR9Tp6"
+ },
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Al_k1rtvQBOM"
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self):\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = 0.05\n",
+ " \n",
+ " # Error\n",
+ " self.int_diff_x1 = 0\n",
+ " self.int_diff_x3 = 0\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 100 * 2 * np.pi / 60\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiskSpeed = 300 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxAngle,self.maxAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, x1_ref, x3_ref, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " # Angle error\n",
+ " diff_x1 = angle_normalize(x1 - x1_ref)\n",
+ " diff_x3 = angle_normalize(x3 - x3_ref)\n",
+ " \n",
+ " # Integral of error\n",
+ " self.int_diff_x1 = self.int_diff_x1 + diff_x1\n",
+ " self.int_diff_x3 = self.int_diff_x3 + diff_x3\n",
+ " \n",
+ " # Reward 1: differentiable reward (LQR obj function)\n",
+ " reward = -((3*diff_x1)**2 + (3*diff_x3)**2 + (.2*x2)**2 + (.2*x4)**2 + (.1*u1)**2 + (.1*u2)**2)\\\n",
+ " #-(0.01*abs(self.int_diff_x1) + 0.01*abs(self.int_diff_x3))\n",
+ "\n",
+ " \"\"\"# Count time spent in goal:\n",
+ " if abs(diff_x1)<0.05 and abs(diff_x3)<0.05:\n",
+ " self.countGoal +=1\n",
+ " else:\n",
+ " self.countGoal = 0\n",
+ " \n",
+ " # Reward 2: sparse reward for staying in goal range for a long time \n",
+ " if self.countGoal >= (self.timeGoal)/self.dt: #max expected reward over length becomes 0 + (totaltime-goaltime)\n",
+ " reward += 1\"\"\"\n",
+ "\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,x1_ref, x3_ref,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, state = None):\n",
+ " \n",
+ " \n",
+ " # Generate random state (for training) or use given state (for simulation)\n",
+ " if state is None:\n",
+ " self.state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " self.state = state\n",
+ "\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = x[-1]\n",
+ "\n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "be0wYIeBQBOc"
+ },
+ "source": [
+ "## Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 533
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 654004,
+ "status": "error",
+ "timestamp": 1584037207187,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "fLyFHs0yQBOd",
+ "outputId": "260489ff-5e40-416a-e529-5a0cfcaefceb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Log dir model already exists! Storing info there anyway.\n",
+ "\u001b[32;1mLogging data to model/progress.txt\u001b[0m\n",
+ "\u001b[36;1mSaving config:\n",
+ "\u001b[0m\n",
+ "{\n",
+ " \"ac_kwargs\":\t{},\n",
+ " \"act_noise\":\t0.1,\n",
+ " \"actor_critic\":\t\"MLPActorCritic\",\n",
+ " \"batch_size\":\t100,\n",
+ " \"env_fn\":\t\"GyroscopeEnv\",\n",
+ " \"epochs\":\t120,\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"gamma\":\t0.95,\n",
+ " \"logger\":\t{\n",
+ " \"<spinup.utils.logx.EpochLogger object at 0x7f7e800ca588>\":\t{\n",
+ " \"epoch_dict\":\t{},\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"first_row\":\ttrue,\n",
+ " \"log_current_row\":\t{},\n",
+ " \"log_headers\":\t[],\n",
+ " \"output_dir\":\t\"model\",\n",
+ " \"output_file\":\t{\n",
+ " \"<_io.TextIOWrapper name='model/progress.txt' mode='w' encoding='UTF-8'>\":\t{\n",
+ " \"mode\":\t\"w\"\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"logger_kwargs\":\t{\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"output_dir\":\t\"model\"\n",
+ " },\n",
+ " \"max_ep_len\":\t110,\n",
+ " \"noise_clip\":\t0.5,\n",
+ " \"num_test_episodes\":\t10,\n",
+ " \"pi_lr\":\t0.001,\n",
+ " \"policy_delay\":\t2,\n",
+ " \"polyak\":\t0.995,\n",
+ " \"q_lr\":\t0.001,\n",
+ " \"replay_size\":\t1000000,\n",
+ " \"save_freq\":\t1,\n",
+ " \"seed\":\t0,\n",
+ " \"start_steps\":\t20000,\n",
+ " \"steps_per_epoch\":\t2200,\n",
+ " \"target_noise\":\t0.2,\n",
+ " \"update_after\":\t800,\n",
+ " \"update_every\":\t50\n",
+ "}\n",
+ "\u001b[32;1m\n",
+ "Number of parameters: \t pi: 68354, \t q1: 68609, \t q2: 68609\n",
+ "\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 1 |\n",
+ "| AverageEpRet | -6.44e+03 |\n",
+ "| StdEpRet | 1.49e+03 |\n",
+ "| MaxEpRet | -4.55e+03 |\n",
+ "| MinEpRet | -9.52e+03 |\n",
+ "| AverageTestEpRet | -7.13e+03 |\n",
+ "| StdTestEpRet | 1.25e+03 |\n",
+ "| MaxTestEpRet | -4.01e+03 |\n",
+ "| MinTestEpRet | -8.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+03 |\n",
+ "| AverageQ1Vals | -123 |\n",
+ "| StdQ1Vals | 54.5 |\n",
+ "| MaxQ1Vals | 0.0809 |\n",
+ "| MinQ1Vals | -364 |\n",
+ "| AverageQ2Vals | -123 |\n",
+ "| StdQ2Vals | 54.6 |\n",
+ "| MaxQ2Vals | 1.77 |\n",
+ "| MinQ2Vals | -373 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 1.95e+03 |\n",
+ "| Time | 15.8 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 2 |\n",
+ "| AverageEpRet | -6.72e+03 |\n",
+ "| StdEpRet | 1.81e+03 |\n",
+ "| MaxEpRet | -4.58e+03 |\n",
+ "| MinEpRet | -1.07e+04 |\n",
+ "| AverageTestEpRet | -8.21e+03 |\n",
+ "| StdTestEpRet | 1.1e+03 |\n",
+ "| MaxTestEpRet | -6.46e+03 |\n",
+ "| MinTestEpRet | -1.01e+04 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+03 |\n",
+ "| AverageQ1Vals | -276 |\n",
+ "| StdQ1Vals | 98.9 |\n",
+ "| MaxQ1Vals | -54.4 |\n",
+ "| MinQ1Vals | -672 |\n",
+ "| AverageQ2Vals | -276 |\n",
+ "| StdQ2Vals | 99.2 |\n",
+ "| MaxQ2Vals | -51.6 |\n",
+ "| MinQ2Vals | -686 |\n",
+ "| LossPi | 256 |\n",
+ "| LossQ | 2.12e+03 |\n",
+ "| Time | 42.1 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 3 |\n",
+ "| AverageEpRet | -7.08e+03 |\n",
+ "| StdEpRet | 1.22e+03 |\n",
+ "| MaxEpRet | -4.94e+03 |\n",
+ "| MinEpRet | -9.27e+03 |\n",
+ "| AverageTestEpRet | -5.91e+03 |\n",
+ "| StdTestEpRet | 1.83e+03 |\n",
+ "| MaxTestEpRet | -3.79e+03 |\n",
+ "| MinTestEpRet | -9.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+03 |\n",
+ "| AverageQ1Vals | -414 |\n",
+ "| StdQ1Vals | 128 |\n",
+ "| MaxQ1Vals | -36.3 |\n",
+ "| MinQ1Vals | -952 |\n",
+ "| AverageQ2Vals | -414 |\n",
+ "| StdQ2Vals | 128 |\n",
+ "| MaxQ2Vals | -26.8 |\n",
+ "| MinQ2Vals | -950 |\n",
+ "| LossPi | 382 |\n",
+ "| LossQ | 2.76e+03 |\n",
+ "| Time | 67.9 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 4 |\n",
+ "| AverageEpRet | -7.06e+03 |\n",
+ "| StdEpRet | 1.53e+03 |\n",
+ "| MaxEpRet | -4.98e+03 |\n",
+ "| MinEpRet | -1.09e+04 |\n",
+ "| AverageTestEpRet | -5.01e+03 |\n",
+ "| StdTestEpRet | 1.18e+03 |\n",
+ "| MaxTestEpRet | -3.34e+03 |\n",
+ "| MinTestEpRet | -6.44e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+03 |\n",
+ "| AverageQ1Vals | -501 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 11.2 |\n",
+ "| MinQ1Vals | -1.07e+03 |\n",
+ "| AverageQ2Vals | -501 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 16.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 459 |\n",
+ "| LossQ | 3.94e+03 |\n",
+ "| Time | 92 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 5 |\n",
+ "| AverageEpRet | -6.3e+03 |\n",
+ "| StdEpRet | 1.14e+03 |\n",
+ "| MaxEpRet | -3.87e+03 |\n",
+ "| MinEpRet | -8.1e+03 |\n",
+ "| AverageTestEpRet | -4.27e+03 |\n",
+ "| StdTestEpRet | 1.52e+03 |\n",
+ "| MaxTestEpRet | -1.82e+03 |\n",
+ "| MinTestEpRet | -6.62e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+04 |\n",
+ "| AverageQ1Vals | -552 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | -15.7 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -552 |\n",
+ "| StdQ2Vals | 172 |\n",
+ "| MaxQ2Vals | 17.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 508 |\n",
+ "| LossQ | 4.63e+03 |\n",
+ "| Time | 116 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 6 |\n",
+ "| AverageEpRet | -6.76e+03 |\n",
+ "| StdEpRet | 1.35e+03 |\n",
+ "| MaxEpRet | -4.61e+03 |\n",
+ "| MinEpRet | -1.13e+04 |\n",
+ "| AverageTestEpRet | -3.67e+03 |\n",
+ "| StdTestEpRet | 994 |\n",
+ "| MaxTestEpRet | -2.24e+03 |\n",
+ "| MinTestEpRet | -5.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+04 |\n",
+ "| AverageQ1Vals | -574 |\n",
+ "| StdQ1Vals | 186 |\n",
+ "| MaxQ1Vals | -39.4 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -574 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | -27.9 |\n",
+ "| MinQ2Vals | -1.29e+03 |\n",
+ "| LossPi | 527 |\n",
+ "| LossQ | 5.18e+03 |\n",
+ "| Time | 139 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 7 |\n",
+ "| AverageEpRet | -7.02e+03 |\n",
+ "| StdEpRet | 1.79e+03 |\n",
+ "| MaxEpRet | -3.34e+03 |\n",
+ "| MinEpRet | -9.5e+03 |\n",
+ "| AverageTestEpRet | -3.05e+03 |\n",
+ "| StdTestEpRet | 1.56e+03 |\n",
+ "| MaxTestEpRet | -1.34e+03 |\n",
+ "| MinTestEpRet | -6.7e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+04 |\n",
+ "| AverageQ1Vals | -582 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | -68.9 |\n",
+ "| MinQ1Vals | -1.38e+03 |\n",
+ "| AverageQ2Vals | -582 |\n",
+ "| StdQ2Vals | 195 |\n",
+ "| MaxQ2Vals | -43.9 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 534 |\n",
+ "| LossQ | 5.26e+03 |\n",
+ "| Time | 163 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 8 |\n",
+ "| AverageEpRet | -6.62e+03 |\n",
+ "| StdEpRet | 1.42e+03 |\n",
+ "| MaxEpRet | -4.04e+03 |\n",
+ "| MinEpRet | -9.02e+03 |\n",
+ "| AverageTestEpRet | -1.54e+03 |\n",
+ "| StdTestEpRet | 771 |\n",
+ "| MaxTestEpRet | -595 |\n",
+ "| MinTestEpRet | -3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+04 |\n",
+ "| AverageQ1Vals | -573 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | -4.68 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -573 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 22.9 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 522 |\n",
+ "| LossQ | 5.51e+03 |\n",
+ "| Time | 187 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 9 |\n",
+ "| AverageEpRet | -6.7e+03 |\n",
+ "| StdEpRet | 1.58e+03 |\n",
+ "| MaxEpRet | -4.21e+03 |\n",
+ "| MinEpRet | -9.46e+03 |\n",
+ "| AverageTestEpRet | -842 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -411 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+04 |\n",
+ "| AverageQ1Vals | -546 |\n",
+ "| StdQ1Vals | 223 |\n",
+ "| MaxQ1Vals | 71.2 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -546 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 60.5 |\n",
+ "| MinQ2Vals | -1.41e+03 |\n",
+ "| LossPi | 490 |\n",
+ "| LossQ | 5.83e+03 |\n",
+ "| Time | 211 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 10 |\n",
+ "| AverageEpRet | -1.66e+03 |\n",
+ "| StdEpRet | 1.99e+03 |\n",
+ "| MaxEpRet | -153 |\n",
+ "| MinEpRet | -8.64e+03 |\n",
+ "| AverageTestEpRet | -872 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -487 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+04 |\n",
+ "| AverageQ1Vals | -488 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 151 |\n",
+ "| MinQ1Vals | -1.43e+03 |\n",
+ "| AverageQ2Vals | -488 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 127 |\n",
+ "| MinQ2Vals | -1.4e+03 |\n",
+ "| LossPi | 432 |\n",
+ "| LossQ | 5.85e+03 |\n",
+ "| Time | 239 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 11 |\n",
+ "| AverageEpRet | -947 |\n",
+ "| StdEpRet | 493 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.89e+03 |\n",
+ "| AverageTestEpRet | -1.09e+03 |\n",
+ "| StdTestEpRet | 661 |\n",
+ "| MaxTestEpRet | -271 |\n",
+ "| MinTestEpRet | -2.78e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+04 |\n",
+ "| AverageQ1Vals | -425 |\n",
+ "| StdQ1Vals | 258 |\n",
+ "| MaxQ1Vals | 141 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -425 |\n",
+ "| StdQ2Vals | 258 |\n",
+ "| MaxQ2Vals | 137 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 376 |\n",
+ "| LossQ | 4.71e+03 |\n",
+ "| Time | 263 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 12 |\n",
+ "| AverageEpRet | -888 |\n",
+ "| StdEpRet | 472 |\n",
+ "| MaxEpRet | -325 |\n",
+ "| MinEpRet | -2.33e+03 |\n",
+ "| AverageTestEpRet | -602 |\n",
+ "| StdTestEpRet | 199 |\n",
+ "| MaxTestEpRet | -286 |\n",
+ "| MinTestEpRet | -910 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+04 |\n",
+ "| AverageQ1Vals | -384 |\n",
+ "| StdQ1Vals | 264 |\n",
+ "| MaxQ1Vals | 161 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -384 |\n",
+ "| StdQ2Vals | 264 |\n",
+ "| MaxQ2Vals | 142 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 341 |\n",
+ "| LossQ | 3.72e+03 |\n",
+ "| Time | 288 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 13 |\n",
+ "| AverageEpRet | -797 |\n",
+ "| StdEpRet | 536 |\n",
+ "| MaxEpRet | -48.6 |\n",
+ "| MinEpRet | -2.46e+03 |\n",
+ "| AverageTestEpRet | -857 |\n",
+ "| StdTestEpRet | 354 |\n",
+ "| MaxTestEpRet | -467 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.86e+04 |\n",
+ "| AverageQ1Vals | -354 |\n",
+ "| StdQ1Vals | 270 |\n",
+ "| MaxQ1Vals | 169 |\n",
+ "| MinQ1Vals | -1.36e+03 |\n",
+ "| AverageQ2Vals | -354 |\n",
+ "| StdQ2Vals | 270 |\n",
+ "| MaxQ2Vals | 166 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 314 |\n",
+ "| LossQ | 3.07e+03 |\n",
+ "| Time | 312 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 14 |\n",
+ "| AverageEpRet | -926 |\n",
+ "| StdEpRet | 418 |\n",
+ "| MaxEpRet | -333 |\n",
+ "| MinEpRet | -1.67e+03 |\n",
+ "| AverageTestEpRet | -902 |\n",
+ "| StdTestEpRet | 481 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -2.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.08e+04 |\n",
+ "| AverageQ1Vals | -326 |\n",
+ "| StdQ1Vals | 274 |\n",
+ "| MaxQ1Vals | 192 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -326 |\n",
+ "| StdQ2Vals | 274 |\n",
+ "| MaxQ2Vals | 191 |\n",
+ "| MinQ2Vals | -1.34e+03 |\n",
+ "| LossPi | 288 |\n",
+ "| LossQ | 2.66e+03 |\n",
+ "| Time | 337 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 15 |\n",
+ "| AverageEpRet | -738 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -267 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -1.45e+03 |\n",
+ "| StdTestEpRet | 1.42e+03 |\n",
+ "| MaxTestEpRet | -185 |\n",
+ "| MinTestEpRet | -4.34e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.3e+04 |\n",
+ "| AverageQ1Vals | -297 |\n",
+ "| StdQ1Vals | 279 |\n",
+ "| MaxQ1Vals | 220 |\n",
+ "| MinQ1Vals | -1.31e+03 |\n",
+ "| AverageQ2Vals | -297 |\n",
+ "| StdQ2Vals | 279 |\n",
+ "| MaxQ2Vals | 221 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 261 |\n",
+ "| LossQ | 2.37e+03 |\n",
+ "| Time | 362 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 16 |\n",
+ "| AverageEpRet | -1.37e+03 |\n",
+ "| StdEpRet | 1.85e+03 |\n",
+ "| MaxEpRet | -223 |\n",
+ "| MinEpRet | -8.63e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 159 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -913 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.52e+04 |\n",
+ "| AverageQ1Vals | -267 |\n",
+ "| StdQ1Vals | 277 |\n",
+ "| MaxQ1Vals | 279 |\n",
+ "| MinQ1Vals | -1.3e+03 |\n",
+ "| AverageQ2Vals | -267 |\n",
+ "| StdQ2Vals | 277 |\n",
+ "| MaxQ2Vals | 269 |\n",
+ "| MinQ2Vals | -1.27e+03 |\n",
+ "| LossPi | 231 |\n",
+ "| LossQ | 2.21e+03 |\n",
+ "| Time | 386 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 17 |\n",
+ "| AverageEpRet | -754 |\n",
+ "| StdEpRet | 595 |\n",
+ "| MaxEpRet | -202 |\n",
+ "| MinEpRet | -2.5e+03 |\n",
+ "| AverageTestEpRet | -636 |\n",
+ "| StdTestEpRet | 389 |\n",
+ "| MaxTestEpRet | -147 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.74e+04 |\n",
+ "| AverageQ1Vals | -245 |\n",
+ "| StdQ1Vals | 273 |\n",
+ "| MaxQ1Vals | 278 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -245 |\n",
+ "| StdQ2Vals | 273 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 211 |\n",
+ "| LossQ | 2.03e+03 |\n",
+ "| Time | 411 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 18 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -241 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -713 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -419 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.96e+04 |\n",
+ "| AverageQ1Vals | -226 |\n",
+ "| StdQ1Vals | 268 |\n",
+ "| MaxQ1Vals | 275 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -226 |\n",
+ "| StdQ2Vals | 268 |\n",
+ "| MaxQ2Vals | 265 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 194 |\n",
+ "| LossQ | 1.83e+03 |\n",
+ "| Time | 434 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 19 |\n",
+ "| AverageEpRet | -745 |\n",
+ "| StdEpRet | 537 |\n",
+ "| MaxEpRet | -109 |\n",
+ "| MinEpRet | -2.14e+03 |\n",
+ "| AverageTestEpRet | -963 |\n",
+ "| StdTestEpRet | 641 |\n",
+ "| MaxTestEpRet | -159 |\n",
+ "| MinTestEpRet | -2.65e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.18e+04 |\n",
+ "| AverageQ1Vals | -214 |\n",
+ "| StdQ1Vals | 263 |\n",
+ "| MaxQ1Vals | 273 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -214 |\n",
+ "| StdQ2Vals | 263 |\n",
+ "| MaxQ2Vals | 266 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 183 |\n",
+ "| LossQ | 1.67e+03 |\n",
+ "| Time | 458 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 20 |\n",
+ "| AverageEpRet | -711 |\n",
+ "| StdEpRet | 511 |\n",
+ "| MaxEpRet | -18.3 |\n",
+ "| MinEpRet | -1.83e+03 |\n",
+ "| AverageTestEpRet | -803 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -126 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+04 |\n",
+ "| AverageQ1Vals | -198 |\n",
+ "| StdQ1Vals | 260 |\n",
+ "| MaxQ1Vals | 276 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -198 |\n",
+ "| StdQ2Vals | 260 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 169 |\n",
+ "| LossQ | 1.57e+03 |\n",
+ "| Time | 483 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 21 |\n",
+ "| AverageEpRet | -664 |\n",
+ "| StdEpRet | 403 |\n",
+ "| MaxEpRet | -176 |\n",
+ "| MinEpRet | -1.87e+03 |\n",
+ "| AverageTestEpRet | -577 |\n",
+ "| StdTestEpRet | 343 |\n",
+ "| MaxTestEpRet | -133 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.62e+04 |\n",
+ "| AverageQ1Vals | -187 |\n",
+ "| StdQ1Vals | 254 |\n",
+ "| MaxQ1Vals | 269 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -187 |\n",
+ "| StdQ2Vals | 254 |\n",
+ "| MaxQ2Vals | 262 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 159 |\n",
+ "| LossQ | 1.48e+03 |\n",
+ "| Time | 507 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 22 |\n",
+ "| AverageEpRet | -662 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -261 |\n",
+ "| MinEpRet | -1.64e+03 |\n",
+ "| AverageTestEpRet | -780 |\n",
+ "| StdTestEpRet | 458 |\n",
+ "| MaxTestEpRet | -39.9 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.84e+04 |\n",
+ "| AverageQ1Vals | -178 |\n",
+ "| StdQ1Vals | 249 |\n",
+ "| MaxQ1Vals | 260 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -178 |\n",
+ "| StdQ2Vals | 249 |\n",
+ "| MaxQ2Vals | 258 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 151 |\n",
+ "| LossQ | 1.35e+03 |\n",
+ "| Time | 531 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 23 |\n",
+ "| AverageEpRet | -524 |\n",
+ "| StdEpRet | 312 |\n",
+ "| MaxEpRet | -85.9 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -768 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -329 |\n",
+ "| MinTestEpRet | -1.3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.06e+04 |\n",
+ "| AverageQ1Vals | -171 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 239 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -170 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 246 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 145 |\n",
+ "| LossQ | 1.29e+03 |\n",
+ "| Time | 555 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 24 |\n",
+ "| AverageEpRet | -714 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -181 |\n",
+ "| MinEpRet | -1.29e+03 |\n",
+ "| AverageTestEpRet | -433 |\n",
+ "| StdTestEpRet | 184 |\n",
+ "| MaxTestEpRet | -178 |\n",
+ "| MinTestEpRet | -786 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.28e+04 |\n",
+ "| AverageQ1Vals | -164 |\n",
+ "| StdQ1Vals | 238 |\n",
+ "| MaxQ1Vals | 227 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -164 |\n",
+ "| StdQ2Vals | 238 |\n",
+ "| MaxQ2Vals | 229 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 139 |\n",
+ "| LossQ | 1.22e+03 |\n",
+ "| Time | 579 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 25 |\n",
+ "| AverageEpRet | -709 |\n",
+ "| StdEpRet | 425 |\n",
+ "| MaxEpRet | -88.8 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -808 |\n",
+ "| StdTestEpRet | 476 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.81e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.5e+04 |\n",
+ "| AverageQ1Vals | -159 |\n",
+ "| StdQ1Vals | 233 |\n",
+ "| MaxQ1Vals | 209 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -159 |\n",
+ "| StdQ2Vals | 233 |\n",
+ "| MaxQ2Vals | 209 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.15e+03 |\n",
+ "| Time | 603 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 26 |\n",
+ "| AverageEpRet | -813 |\n",
+ "| StdEpRet | 433 |\n",
+ "| MaxEpRet | -247 |\n",
+ "| MinEpRet | -1.81e+03 |\n",
+ "| AverageTestEpRet | -702 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.72e+04 |\n",
+ "| AverageQ1Vals | -158 |\n",
+ "| StdQ1Vals | 231 |\n",
+ "| MaxQ1Vals | 194 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -158 |\n",
+ "| StdQ2Vals | 231 |\n",
+ "| MaxQ2Vals | 198 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.14e+03 |\n",
+ "| Time | 628 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 27 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 282 |\n",
+ "| MaxEpRet | -336 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -934 |\n",
+ "| StdTestEpRet | 626 |\n",
+ "| MaxTestEpRet | -317 |\n",
+ "| MinTestEpRet | -2.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.94e+04 |\n",
+ "| AverageQ1Vals | -157 |\n",
+ "| StdQ1Vals | 229 |\n",
+ "| MaxQ1Vals | 190 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -157 |\n",
+ "| StdQ2Vals | 229 |\n",
+ "| MaxQ2Vals | 192 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 135 |\n",
+ "| LossQ | 1.03e+03 |\n",
+ "| Time | 653 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 28 |\n",
+ "| AverageEpRet | -965 |\n",
+ "| StdEpRet | 522 |\n",
+ "| MaxEpRet | -172 |\n",
+ "| MinEpRet | -2.3e+03 |\n",
+ "| AverageTestEpRet | -599 |\n",
+ "| StdTestEpRet | 398 |\n",
+ "| MaxTestEpRet | -163 |\n",
+ "| MinTestEpRet | -1.32e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.16e+04 |\n",
+ "| AverageQ1Vals | -154 |\n",
+ "| StdQ1Vals | 226 |\n",
+ "| MaxQ1Vals | 176 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -154 |\n",
+ "| StdQ2Vals | 226 |\n",
+ "| MaxQ2Vals | 188 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 973 |\n",
+ "| Time | 677 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 29 |\n",
+ "| AverageEpRet | -766 |\n",
+ "| StdEpRet | 367 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.69e+03 |\n",
+ "| AverageTestEpRet | -659 |\n",
+ "| StdTestEpRet | 229 |\n",
+ "| MaxTestEpRet | -303 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.38e+04 |\n",
+ "| AverageQ1Vals | -153 |\n",
+ "| StdQ1Vals | 225 |\n",
+ "| MaxQ1Vals | 173 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -153 |\n",
+ "| StdQ2Vals | 225 |\n",
+ "| MaxQ2Vals | 176 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 961 |\n",
+ "| Time | 702 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 30 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -388 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -686 |\n",
+ "| StdTestEpRet | 188 |\n",
+ "| MaxTestEpRet | -312 |\n",
+ "| MinTestEpRet | -977 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+04 |\n",
+ "| AverageQ1Vals | -151 |\n",
+ "| StdQ1Vals | 222 |\n",
+ "| MaxQ1Vals | 162 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -151 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 159 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 131 |\n",
+ "| LossQ | 913 |\n",
+ "| Time | 727 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 31 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 442 |\n",
+ "| MaxEpRet | -163 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -678 |\n",
+ "| StdTestEpRet | 371 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.82e+04 |\n",
+ "| AverageQ1Vals | -149 |\n",
+ "| StdQ1Vals | 219 |\n",
+ "| MaxQ1Vals | 149 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -149 |\n",
+ "| StdQ2Vals | 219 |\n",
+ "| MaxQ2Vals | 153 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 855 |\n",
+ "| Time | 753 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 32 |\n",
+ "| AverageEpRet | -760 |\n",
+ "| StdEpRet | 313 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -1.49e+03 |\n",
+ "| AverageTestEpRet | -759 |\n",
+ "| StdTestEpRet | 430 |\n",
+ "| MaxTestEpRet | -256 |\n",
+ "| MinTestEpRet | -1.64e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.04e+04 |\n",
+ "| AverageQ1Vals | -150 |\n",
+ "| StdQ1Vals | 217 |\n",
+ "| MaxQ1Vals | 146 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -150 |\n",
+ "| StdQ2Vals | 217 |\n",
+ "| MaxQ2Vals | 145 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 835 |\n",
+ "| Time | 775 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 33 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 395 |\n",
+ "| MaxEpRet | -57.1 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 368 |\n",
+ "| MaxTestEpRet | -120 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.26e+04 |\n",
+ "| AverageQ1Vals | -147 |\n",
+ "| StdQ1Vals | 213 |\n",
+ "| MaxQ1Vals | 130 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -147 |\n",
+ "| StdQ2Vals | 213 |\n",
+ "| MaxQ2Vals | 125 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 129 |\n",
+ "| LossQ | 800 |\n",
+ "| Time | 796 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 34 |\n",
+ "| AverageEpRet | -732 |\n",
+ "| StdEpRet | 361 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.6e+03 |\n",
+ "| AverageTestEpRet | -737 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -278 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.48e+04 |\n",
+ "| AverageQ1Vals | -144 |\n",
+ "| StdQ1Vals | 211 |\n",
+ "| MaxQ1Vals | 133 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -144 |\n",
+ "| StdQ2Vals | 211 |\n",
+ "| MaxQ2Vals | 121 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 126 |\n",
+ "| LossQ | 784 |\n",
+ "| Time | 820 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 35 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -159 |\n",
+ "| MinEpRet | -1.58e+03 |\n",
+ "| AverageTestEpRet | -766 |\n",
+ "| StdTestEpRet | 358 |\n",
+ "| MaxTestEpRet | -85.9 |\n",
+ "| MinTestEpRet | -1.35e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.7e+04 |\n",
+ "| AverageQ1Vals | -143 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -143 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 107 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 730 |\n",
+ "| Time | 846 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 36 |\n",
+ "| AverageEpRet | -848 |\n",
+ "| StdEpRet | 733 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -3.32e+03 |\n",
+ "| AverageTestEpRet | -765 |\n",
+ "| StdTestEpRet | 340 |\n",
+ "| MaxTestEpRet | -191 |\n",
+ "| MinTestEpRet | -1.48e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.92e+04 |\n",
+ "| AverageQ1Vals | -141 |\n",
+ "| StdQ1Vals | 207 |\n",
+ "| MaxQ1Vals | 115 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -141 |\n",
+ "| StdQ2Vals | 207 |\n",
+ "| MaxQ2Vals | 108 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 703 |\n",
+ "| Time | 873 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 37 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 379 |\n",
+ "| MaxEpRet | -11.3 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -646 |\n",
+ "| StdTestEpRet | 399 |\n",
+ "| MaxTestEpRet | -102 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.14e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 204 |\n",
+ "| MaxQ1Vals | 103 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 204 |\n",
+ "| MaxQ2Vals | 96.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 667 |\n",
+ "| Time | 900 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 38 |\n",
+ "| AverageEpRet | -634 |\n",
+ "| StdEpRet | 304 |\n",
+ "| MaxEpRet | -44.1 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -716 |\n",
+ "| StdTestEpRet | 356 |\n",
+ "| MaxTestEpRet | -263 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.36e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 203 |\n",
+ "| MaxQ1Vals | 91.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 203 |\n",
+ "| MaxQ2Vals | 86 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 664 |\n",
+ "| Time | 929 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 39 |\n",
+ "| AverageEpRet | -630 |\n",
+ "| StdEpRet | 375 |\n",
+ "| MaxEpRet | -127 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -591 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -204 |\n",
+ "| MinTestEpRet | -946 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.58e+04 |\n",
+ "| AverageQ1Vals | -139 |\n",
+ "| StdQ1Vals | 201 |\n",
+ "| MaxQ1Vals | 86.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -139 |\n",
+ "| StdQ2Vals | 201 |\n",
+ "| MaxQ2Vals | 77.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 649 |\n",
+ "| Time | 960 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 40 |\n",
+ "| AverageEpRet | -506 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -67.2 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -621 |\n",
+ "| StdTestEpRet | 351 |\n",
+ "| MaxTestEpRet | -171 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+04 |\n",
+ "| AverageQ1Vals | -137 |\n",
+ "| StdQ1Vals | 199 |\n",
+ "| MaxQ1Vals | 77.5 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -137 |\n",
+ "| StdQ2Vals | 199 |\n",
+ "| MaxQ2Vals | 66.2 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 122 |\n",
+ "| LossQ | 629 |\n",
+ "| Time | 991 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 41 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 301 |\n",
+ "| MaxEpRet | -182 |\n",
+ "| MinEpRet | -1.31e+03 |\n",
+ "| AverageTestEpRet | -784 |\n",
+ "| StdTestEpRet | 309 |\n",
+ "| MaxTestEpRet | -408 |\n",
+ "| MinTestEpRet | -1.4e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.02e+04 |\n",
+ "| AverageQ1Vals | -136 |\n",
+ "| StdQ1Vals | 197 |\n",
+ "| MaxQ1Vals | 74.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -136 |\n",
+ "| StdQ2Vals | 197 |\n",
+ "| MaxQ2Vals | 74.3 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 121 |\n",
+ "| LossQ | 587 |\n",
+ "| Time | 1.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 42 |\n",
+ "| AverageEpRet | -623 |\n",
+ "| StdEpRet | 295 |\n",
+ "| MaxEpRet | -69.3 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -806 |\n",
+ "| StdTestEpRet | 386 |\n",
+ "| MaxTestEpRet | -285 |\n",
+ "| MinTestEpRet | -1.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.24e+04 |\n",
+ "| AverageQ1Vals | -134 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | 59.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -134 |\n",
+ "| StdQ2Vals | 196 |\n",
+ "| MaxQ2Vals | 67.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 120 |\n",
+ "| LossQ | 584 |\n",
+ "| Time | 1.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 43 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 255 |\n",
+ "| MaxEpRet | -79.6 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -620 |\n",
+ "| StdTestEpRet | 300 |\n",
+ "| MaxTestEpRet | -160 |\n",
+ "| MinTestEpRet | -971 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.46e+04 |\n",
+ "| AverageQ1Vals | -133 |\n",
+ "| StdQ1Vals | 194 |\n",
+ "| MaxQ1Vals | 63.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -133 |\n",
+ "| StdQ2Vals | 194 |\n",
+ "| MaxQ2Vals | 68.2 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 561 |\n",
+ "| Time | 1.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 44 |\n",
+ "| AverageEpRet | -583 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -63.3 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -507 |\n",
+ "| StdTestEpRet | 296 |\n",
+ "| MaxTestEpRet | -107 |\n",
+ "| MinTestEpRet | -996 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.68e+04 |\n",
+ "| AverageQ1Vals | -132 |\n",
+ "| StdQ1Vals | 191 |\n",
+ "| MaxQ1Vals | 49.2 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -132 |\n",
+ "| StdQ2Vals | 191 |\n",
+ "| MaxQ2Vals | 49.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 532 |\n",
+ "| Time | 1.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 45 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 309 |\n",
+ "| MaxEpRet | -147 |\n",
+ "| MinEpRet | -1.23e+03 |\n",
+ "| AverageTestEpRet | -727 |\n",
+ "| StdTestEpRet | 348 |\n",
+ "| MaxTestEpRet | -203 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.9e+04 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 50.6 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 47.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 116 |\n",
+ "| LossQ | 517 |\n",
+ "| Time | 1.14e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 46 |\n",
+ "| AverageEpRet | -649 |\n",
+ "| StdEpRet | 325 |\n",
+ "| MaxEpRet | -151 |\n",
+ "| MinEpRet | -1.5e+03 |\n",
+ "| AverageTestEpRet | -647 |\n",
+ "| StdTestEpRet | 317 |\n",
+ "| MaxTestEpRet | -219 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.01e+05 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 90.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 115 |\n",
+ "| LossQ | 514 |\n",
+ "| Time | 1.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 47 |\n",
+ "| AverageEpRet | -444 |\n",
+ "| StdEpRet | 183 |\n",
+ "| MaxEpRet | -183 |\n",
+ "| MinEpRet | -968 |\n",
+ "| AverageTestEpRet | -669 |\n",
+ "| StdTestEpRet | 245 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.12e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.03e+05 |\n",
+ "| AverageQ1Vals | -127 |\n",
+ "| StdQ1Vals | 185 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -127 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | 55.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 113 |\n",
+ "| LossQ | 489 |\n",
+ "| Time | 1.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 48 |\n",
+ "| AverageEpRet | -673 |\n",
+ "| StdEpRet | 332 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.41e+03 |\n",
+ "| AverageTestEpRet | -534 |\n",
+ "| StdTestEpRet | 252 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.06e+05 |\n",
+ "| AverageQ1Vals | -125 |\n",
+ "| StdQ1Vals | 182 |\n",
+ "| MaxQ1Vals | 96.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -125 |\n",
+ "| StdQ2Vals | 182 |\n",
+ "| MaxQ2Vals | 93.5 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 473 |\n",
+ "| Time | 1.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 49 |\n",
+ "| AverageEpRet | -475 |\n",
+ "| StdEpRet | 243 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -572 |\n",
+ "| StdTestEpRet | 339 |\n",
+ "| MaxTestEpRet | -155 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.08e+05 |\n",
+ "| AverageQ1Vals | -124 |\n",
+ "| StdQ1Vals | 181 |\n",
+ "| MaxQ1Vals | 66.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -124 |\n",
+ "| StdQ2Vals | 181 |\n",
+ "| MaxQ2Vals | 60.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 466 |\n",
+ "| Time | 1.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 50 |\n",
+ "| AverageEpRet | -581 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -121 |\n",
+ "| MinEpRet | -1.34e+03 |\n",
+ "| AverageTestEpRet | -687 |\n",
+ "| StdTestEpRet | 246 |\n",
+ "| MaxTestEpRet | -246 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 50.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 439 |\n",
+ "| Time | 1.31e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 51 |\n",
+ "| AverageEpRet | -599 |\n",
+ "| StdEpRet | 287 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 204 |\n",
+ "| MaxTestEpRet | -216 |\n",
+ "| MinTestEpRet | -856 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.12e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 78.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 76.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 110 |\n",
+ "| LossQ | 437 |\n",
+ "| Time | 1.34e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 52 |\n",
+ "| AverageEpRet | -636 |\n",
+ "| StdEpRet | 283 |\n",
+ "| MaxEpRet | -220 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -712 |\n",
+ "| StdTestEpRet | 185 |\n",
+ "| MaxTestEpRet | -457 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.14e+05 |\n",
+ "| AverageQ1Vals | -121 |\n",
+ "| StdQ1Vals | 178 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -121 |\n",
+ "| StdQ2Vals | 178 |\n",
+ "| MaxQ2Vals | 54.7 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 431 |\n",
+ "| Time | 1.37e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 53 |\n",
+ "| AverageEpRet | -591 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -132 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -551 |\n",
+ "| StdTestEpRet | 206 |\n",
+ "| MaxTestEpRet | -333 |\n",
+ "| MinTestEpRet | -975 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.17e+05 |\n",
+ "| AverageQ1Vals | -119 |\n",
+ "| StdQ1Vals | 176 |\n",
+ "| MaxQ1Vals | 54.4 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -119 |\n",
+ "| StdQ2Vals | 176 |\n",
+ "| MaxQ2Vals | 89.1 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 108 |\n",
+ "| LossQ | 418 |\n",
+ "| Time | 1.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 54 |\n",
+ "| AverageEpRet | -426 |\n",
+ "| StdEpRet | 230 |\n",
+ "| MaxEpRet | -90.2 |\n",
+ "| MinEpRet | -906 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 197 |\n",
+ "| MaxTestEpRet | -124 |\n",
+ "| MinTestEpRet | -708 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.19e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 175 |\n",
+ "| MaxQ1Vals | 47.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 175 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 401 |\n",
+ "| Time | 1.43e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 55 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -939 |\n",
+ "| AverageTestEpRet | -615 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -400 |\n",
+ "| MinTestEpRet | -952 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.21e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 174 |\n",
+ "| MaxQ1Vals | 54.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 174 |\n",
+ "| MaxQ2Vals | 68.6 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 392 |\n",
+ "| Time | 1.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 56 |\n",
+ "| AverageEpRet | -552 |\n",
+ "| StdEpRet | 234 |\n",
+ "| MaxEpRet | -228 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -400 |\n",
+ "| StdTestEpRet | 293 |\n",
+ "| MaxTestEpRet | -66.3 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.23e+05 |\n",
+ "| AverageQ1Vals | -117 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | 66.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -117 |\n",
+ "| StdQ2Vals | 173 |\n",
+ "| MaxQ2Vals | 69.5 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 105 |\n",
+ "| LossQ | 400 |\n",
+ "| Time | 1.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 57 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 318 |\n",
+ "| MaxEpRet | -33.7 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -480 |\n",
+ "| StdTestEpRet | 210 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -802 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.25e+05 |\n",
+ "| AverageQ1Vals | -114 |\n",
+ "| StdQ1Vals | 171 |\n",
+ "| MaxQ1Vals | 68.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -114 |\n",
+ "| StdQ2Vals | 171 |\n",
+ "| MaxQ2Vals | 68.8 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 103 |\n",
+ "| LossQ | 380 |\n",
+ "| Time | 1.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 58 |\n",
+ "| AverageEpRet | -590 |\n",
+ "| StdEpRet | 261 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 234 |\n",
+ "| MaxTestEpRet | -206 |\n",
+ "| MinTestEpRet | -935 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.28e+05 |\n",
+ "| AverageQ1Vals | -113 |\n",
+ "| StdQ1Vals | 170 |\n",
+ "| MaxQ1Vals | 72.9 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -113 |\n",
+ "| StdQ2Vals | 170 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 368 |\n",
+ "| Time | 1.56e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 59 |\n",
+ "| AverageEpRet | -588 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -1.05e+03 |\n",
+ "| AverageTestEpRet | -467 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -763 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.3e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 169 |\n",
+ "| MaxQ1Vals | 64.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 169 |\n",
+ "| MaxQ2Vals | 93.3 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 367 |\n",
+ "| Time | 1.6e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 60 |\n",
+ "| AverageEpRet | -495 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -44.8 |\n",
+ "| MinEpRet | -1.71e+03 |\n",
+ "| AverageTestEpRet | -654 |\n",
+ "| StdTestEpRet | 261 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.05e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 168 |\n",
+ "| MaxQ1Vals | 68.9 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 168 |\n",
+ "| MaxQ2Vals | 73.7 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 101 |\n",
+ "| LossQ | 363 |\n",
+ "| Time | 1.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 61 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 376 |\n",
+ "| MaxEpRet | -74.9 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -574 |\n",
+ "| StdTestEpRet | 410 |\n",
+ "| MaxTestEpRet | -144 |\n",
+ "| MinTestEpRet | -1.57e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.34e+05 |\n",
+ "| AverageQ1Vals | -110 |\n",
+ "| StdQ1Vals | 166 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -110 |\n",
+ "| StdQ2Vals | 166 |\n",
+ "| MaxQ2Vals | 52.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 100 |\n",
+ "| LossQ | 349 |\n",
+ "| Time | 1.66e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 62 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -485 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -886 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.36e+05 |\n",
+ "| AverageQ1Vals | -109 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 67.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -109 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 98.7 |\n",
+ "| LossQ | 358 |\n",
+ "| Time | 1.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 63 |\n",
+ "| AverageEpRet | -448 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -56.4 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -341 |\n",
+ "| StdTestEpRet | 181 |\n",
+ "| MaxTestEpRet | -63.6 |\n",
+ "| MinTestEpRet | -608 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.39e+05 |\n",
+ "| AverageQ1Vals | -108 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -108 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 52.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 97.8 |\n",
+ "| LossQ | 344 |\n",
+ "| Time | 1.73e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 64 |\n",
+ "| AverageEpRet | -525 |\n",
+ "| StdEpRet | 226 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -532 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -302 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.41e+05 |\n",
+ "| AverageQ1Vals | -106 |\n",
+ "| StdQ1Vals | 164 |\n",
+ "| MaxQ1Vals | 50.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -106 |\n",
+ "| StdQ2Vals | 164 |\n",
+ "| MaxQ2Vals | 37.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 95.7 |\n",
+ "| LossQ | 340 |\n",
+ "| Time | 1.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 65 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -31.2 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -520 |\n",
+ "| StdTestEpRet | 359 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -1.51e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.43e+05 |\n",
+ "| AverageQ1Vals | -105 |\n",
+ "| StdQ1Vals | 162 |\n",
+ "| MaxQ1Vals | 58.3 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -105 |\n",
+ "| StdQ2Vals | 162 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 95.2 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 66 |\n",
+ "| AverageEpRet | -622 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -126 |\n",
+ "| MinEpRet | -1.33e+03 |\n",
+ "| AverageTestEpRet | -525 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.45e+05 |\n",
+ "| AverageQ1Vals | -104 |\n",
+ "| StdQ1Vals | 163 |\n",
+ "| MaxQ1Vals | 46.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -104 |\n",
+ "| StdQ2Vals | 163 |\n",
+ "| MaxQ2Vals | 73.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 94.4 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.83e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 67 |\n",
+ "| AverageEpRet | -483 |\n",
+ "| StdEpRet | 204 |\n",
+ "| MaxEpRet | -118 |\n",
+ "| MinEpRet | -836 |\n",
+ "| AverageTestEpRet | -530 |\n",
+ "| StdTestEpRet | 231 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -994 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.47e+05 |\n",
+ "| AverageQ1Vals | -103 |\n",
+ "| StdQ1Vals | 161 |\n",
+ "| MaxQ1Vals | 56 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -103 |\n",
+ "| StdQ2Vals | 161 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 93.2 |\n",
+ "| LossQ | 325 |\n",
+ "| Time | 1.86e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 68 |\n",
+ "| AverageEpRet | -443 |\n",
+ "| StdEpRet | 266 |\n",
+ "| MaxEpRet | -17.7 |\n",
+ "| MinEpRet | -926 |\n",
+ "| AverageTestEpRet | -498 |\n",
+ "| StdTestEpRet | 213 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -779 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.5e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 71.7 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 76.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.3 |\n",
+ "| LossQ | 311 |\n",
+ "| Time | 1.89e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 69 |\n",
+ "| AverageEpRet | -542 |\n",
+ "| StdEpRet | 422 |\n",
+ "| MaxEpRet | -74.6 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 205 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -905 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.52e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 57.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 106 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.1 |\n",
+ "| LossQ | 315 |\n",
+ "| Time | 1.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 70 |\n",
+ "| AverageEpRet | -556 |\n",
+ "| StdEpRet | 188 |\n",
+ "| MaxEpRet | -166 |\n",
+ "| MinEpRet | -827 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -247 |\n",
+ "| MinTestEpRet | -782 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 104 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 89.6 |\n",
+ "| LossQ | 298 |\n",
+ "| Time | 1.96e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 71 |\n",
+ "| AverageEpRet | -508 |\n",
+ "| StdEpRet | 360 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.65e+03 |\n",
+ "| AverageTestEpRet | -503 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -98.4 |\n",
+ "| MinTestEpRet | -933 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.56e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 68.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 62.4 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.9 |\n",
+ "| LossQ | 310 |\n",
+ "| Time | 1.98e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 72 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -154 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 299 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.58e+05 |\n",
+ "| AverageQ1Vals | -98.4 |\n",
+ "| StdQ1Vals | 157 |\n",
+ "| MaxQ1Vals | 40.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -98.4 |\n",
+ "| StdQ2Vals | 157 |\n",
+ "| MaxQ2Vals | 65.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 307 |\n",
+ "| Time | 2.01e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 73 |\n",
+ "| AverageEpRet | -531 |\n",
+ "| StdEpRet | 253 |\n",
+ "| MaxEpRet | -152 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -403 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -66.2 |\n",
+ "| MinTestEpRet | -865 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.61e+05 |\n",
+ "| AverageQ1Vals | -98 |\n",
+ "| StdQ1Vals | 156 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -98 |\n",
+ "| StdQ2Vals | 156 |\n",
+ "| MaxQ2Vals | 61.2 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 290 |\n",
+ "| Time | 2.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 74 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 225 |\n",
+ "| MaxEpRet | -242 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -421 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -972 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.63e+05 |\n",
+ "| AverageQ1Vals | -96.8 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 84.5 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -96.8 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 44 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 87.6 |\n",
+ "| LossQ | 280 |\n",
+ "| Time | 2.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 75 |\n",
+ "| AverageEpRet | -438 |\n",
+ "| StdEpRet | 207 |\n",
+ "| MaxEpRet | -103 |\n",
+ "| MinEpRet | -930 |\n",
+ "| AverageTestEpRet | -416 |\n",
+ "| StdTestEpRet | 241 |\n",
+ "| MaxTestEpRet | -135 |\n",
+ "| MinTestEpRet | -887 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.65e+05 |\n",
+ "| AverageQ1Vals | -96.7 |\n",
+ "| StdQ1Vals | 155 |\n",
+ "| MaxQ1Vals | 72.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -96.7 |\n",
+ "| StdQ2Vals | 155 |\n",
+ "| MaxQ2Vals | 75.7 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 88.1 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 76 |\n",
+ "| AverageEpRet | -639 |\n",
+ "| StdEpRet | 315 |\n",
+ "| MaxEpRet | -214 |\n",
+ "| MinEpRet | -1.51e+03 |\n",
+ "| AverageTestEpRet | -437 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -19.2 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.67e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 48.5 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 46.2 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 86.8 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.14e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 77 |\n",
+ "| AverageEpRet | -468 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -901 |\n",
+ "| AverageTestEpRet | -556 |\n",
+ "| StdTestEpRet | 262 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -812 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.69e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 37.2 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 49.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.9 |\n",
+ "| LossQ | 283 |\n",
+ "| Time | 2.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 78 |\n",
+ "| AverageEpRet | -568 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -123 |\n",
+ "| MinEpRet | -1.27e+03 |\n",
+ "| AverageTestEpRet | -550 |\n",
+ "| StdTestEpRet | 360 |\n",
+ "| MaxTestEpRet | -164 |\n",
+ "| MinTestEpRet | -1.26e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.72e+05 |\n",
+ "| AverageQ1Vals | -94.5 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 47.8 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -94.5 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 92 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 85.9 |\n",
+ "| LossQ | 265 |\n",
+ "| Time | 2.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 79 |\n",
+ "| AverageEpRet | -566 |\n",
+ "| StdEpRet | 337 |\n",
+ "| MaxEpRet | -96.5 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -543 |\n",
+ "| StdTestEpRet | 365 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.74e+05 |\n",
+ "| AverageQ1Vals | -94.8 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 69.7 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -94.8 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 75.6 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 278 |\n",
+ "| Time | 2.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 80 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 290 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 196 |\n",
+ "| MaxTestEpRet | -182 |\n",
+ "| MinTestEpRet | -783 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+05 |\n",
+ "| AverageQ1Vals | -94.6 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 59.6 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -94.6 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 74.6 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 271 |\n",
+ "| Time | 2.28e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 81 |\n",
+ "| AverageEpRet | -535 |\n",
+ "| StdEpRet | 217 |\n",
+ "| MaxEpRet | -131 |\n",
+ "| MinEpRet | -999 |\n",
+ "| AverageTestEpRet | -422 |\n",
+ "| StdTestEpRet | 270 |\n",
+ "| MaxTestEpRet | -67.3 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.78e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 150 |\n",
+ "| MaxQ1Vals | 47.4 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 150 |\n",
+ "| MaxQ2Vals | 71 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 85.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.31e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 82 |\n",
+ "| AverageEpRet | -505 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -79.9 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -734 |\n",
+ "| StdTestEpRet | 289 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.22e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.8e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 149 |\n",
+ "| MaxQ1Vals | 53 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 149 |\n",
+ "| MaxQ2Vals | 67.3 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 86.6 |\n",
+ "| LossQ | 267 |\n",
+ "| Time | 2.35e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 83 |\n",
+ "| AverageEpRet | -585 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -96.7 |\n",
+ "| MinEpRet | -1.3e+03 |\n",
+ "| AverageTestEpRet | -436 |\n",
+ "| StdTestEpRet | 169 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -748 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.83e+05 |\n",
+ "| AverageQ1Vals | -92.9 |\n",
+ "| StdQ1Vals | 147 |\n",
+ "| MaxQ1Vals | 44.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -92.9 |\n",
+ "| StdQ2Vals | 147 |\n",
+ "| MaxQ2Vals | 75.2 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 84.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.38e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 84 |\n",
+ "| AverageEpRet | -406 |\n",
+ "| StdEpRet | 232 |\n",
+ "| MaxEpRet | -128 |\n",
+ "| MinEpRet | -924 |\n",
+ "| AverageTestEpRet | -650 |\n",
+ "| StdTestEpRet | 236 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.85e+05 |\n",
+ "| AverageQ1Vals | -92.5 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 46.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -92.5 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 80.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 84.4 |\n",
+ "| LossQ | 258 |\n",
+ "| Time | 2.41e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 85 |\n",
+ "| AverageEpRet | -474 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -180 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -527 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -166 |\n",
+ "| MinTestEpRet | -737 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.87e+05 |\n",
+ "| AverageQ1Vals | -91.8 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 51.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -91.8 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 33.2 |\n",
+ "| MinQ2Vals | -1.06e+03 |\n",
+ "| LossPi | 84.1 |\n",
+ "| LossQ | 249 |\n",
+ "| Time | 2.44e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 86 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 264 |\n",
+ "| MaxEpRet | -112 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -665 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.89e+05 |\n",
+ "| AverageQ1Vals | -91.6 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 65.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -91.7 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 67.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 83.5 |\n",
+ "| LossQ | 238 |\n",
+ "| Time | 2.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 87 |\n",
+ "| AverageEpRet | -515 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -122 |\n",
+ "| MinEpRet | -976 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 202 |\n",
+ "| MaxTestEpRet | -180 |\n",
+ "| MinTestEpRet | -829 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.91e+05 |\n",
+ "| AverageQ1Vals | -92.1 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -92.1 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 54.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 83.6 |\n",
+ "| LossQ | 256 |\n",
+ "| Time | 2.51e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 88 |\n",
+ "| AverageEpRet | -572 |\n",
+ "| StdEpRet | 258 |\n",
+ "| MaxEpRet | -57 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -508 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -217 |\n",
+ "| MinTestEpRet | -794 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.94e+05 |\n",
+ "| AverageQ1Vals | -91.3 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 42.6 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -91.3 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 58 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 83.7 |\n",
+ "| LossQ | 246 |\n",
+ "| Time | 2.54e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 89 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 241 |\n",
+ "| MaxEpRet | -49.9 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -562 |\n",
+ "| StdTestEpRet | 306 |\n",
+ "| MaxTestEpRet | -68.2 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.96e+05 |\n",
+ "| AverageQ1Vals | -90.3 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -90.3 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 54.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 83 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.57e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 90 |\n",
+ "| AverageEpRet | -478 |\n",
+ "| StdEpRet | 286 |\n",
+ "| MaxEpRet | -68 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -603 |\n",
+ "| StdTestEpRet | 390 |\n",
+ "| MaxTestEpRet | -123 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.6 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.6e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 91 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 252 |\n",
+ "| MaxEpRet | -101 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -566 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -62.9 |\n",
+ "| MinTestEpRet | -1.02e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 25.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.7 |\n",
+ "| LossQ | 241 |\n",
+ "| Time | 2.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 92 |\n",
+ "| AverageEpRet | -425 |\n",
+ "| StdEpRet | 205 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -998 |\n",
+ "| AverageTestEpRet | -536 |\n",
+ "| StdTestEpRet | 271 |\n",
+ "| MaxTestEpRet | -209 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.02e+05 |\n",
+ "| AverageQ1Vals | -88.7 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -88.7 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 32.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 81.3 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.67e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 93 |\n",
+ "| AverageEpRet | -498 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -83.8 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -523 |\n",
+ "| StdTestEpRet | 286 |\n",
+ "| MaxTestEpRet | -83.5 |\n",
+ "| MinTestEpRet | -967 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.05e+05 |\n",
+ "| AverageQ1Vals | -88.2 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 97.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -88.2 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 36.8 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 80.8 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 94 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 231 |\n",
+ "| MaxEpRet | -88.9 |\n",
+ "| MinEpRet | -946 |\n",
+ "| AverageTestEpRet | -407 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -793 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.07e+05 |\n",
+ "| AverageQ1Vals | -87.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -87.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.6 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 80.4 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.73e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 95 |\n",
+ "| AverageEpRet | -471 |\n",
+ "| StdEpRet | 257 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -994 |\n",
+ "| AverageTestEpRet | -563 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -827 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.09e+05 |\n",
+ "| AverageQ1Vals | -87.2 |\n",
+ "| StdQ1Vals | 141 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -87.2 |\n",
+ "| StdQ2Vals | 141 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 80.3 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 96 |\n",
+ "| AverageEpRet | -473 |\n",
+ "| StdEpRet | 268 |\n",
+ "| MaxEpRet | -76.1 |\n",
+ "| MinEpRet | -1.03e+03 |\n",
+ "| AverageTestEpRet | -392 |\n",
+ "| StdTestEpRet | 191 |\n",
+ "| MaxTestEpRet | -90.2 |\n",
+ "| MinTestEpRet | -713 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.11e+05 |\n",
+ "| AverageQ1Vals | -86.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 32.9 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -86.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 40.5 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 79.5 |\n",
+ "| LossQ | 233 |\n",
+ "| Time | 2.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 97 |\n",
+ "| AverageEpRet | -497 |\n",
+ "| StdEpRet | 345 |\n",
+ "| MaxEpRet | -37.1 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 227 |\n",
+ "| MaxTestEpRet | -72.6 |\n",
+ "| MinTestEpRet | -742 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.13e+05 |\n",
+ "| AverageQ1Vals | -86.5 |\n",
+ "| StdQ1Vals | 140 |\n",
+ "| MaxQ1Vals | 43 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -86.5 |\n",
+ "| StdQ2Vals | 140 |\n",
+ "| MaxQ2Vals | 38.9 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 79.6 |\n",
+ "| LossQ | 234 |\n",
+ "| Time | 2.82e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 98 |\n",
+ "| AverageEpRet | -429 |\n",
+ "| StdEpRet | 215 |\n",
+ "| MaxEpRet | -135 |\n",
+ "| MinEpRet | -848 |\n",
+ "| AverageTestEpRet | -511 |\n",
+ "| StdTestEpRet | 337 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -1.37e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.16e+05 |\n",
+ "| AverageQ1Vals | -86.1 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -86.1 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43.7 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 79.1 |\n",
+ "| LossQ | 226 |\n",
+ "| Time | 2.85e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 99 |\n",
+ "| AverageEpRet | -514 |\n",
+ "| StdEpRet | 278 |\n",
+ "| MaxEpRet | -141 |\n",
+ "| MinEpRet | -1.21e+03 |\n",
+ "| AverageTestEpRet | -633 |\n",
+ "| StdTestEpRet | 489 |\n",
+ "| MaxTestEpRet | -181 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.18e+05 |\n",
+ "| AverageQ1Vals | -84.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -84.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 27.5 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 77.7 |\n",
+ "| LossQ | 225 |\n",
+ "| Time | 2.88e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 100 |\n",
+ "| AverageEpRet | -557 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -26 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -544 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -1.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+05 |\n",
+ "| AverageQ1Vals | -84 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 51.4 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -84 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43 |\n",
+ "| MinQ2Vals | -1.22e+03 |\n",
+ "| LossPi | 76.6 |\n",
+ "| LossQ | 220 |\n",
+ "| Time | 2.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 101 |\n",
+ "| AverageEpRet | -434 |\n",
+ "| StdEpRet | 192 |\n",
+ "| MaxEpRet | -155 |\n",
+ "| MinEpRet | -806 |\n",
+ "| AverageTestEpRet | -418 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -244 |\n",
+ "| MinTestEpRet | -730 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.22e+05 |\n",
+ "| AverageQ1Vals | -82.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 35.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -82.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 54.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 76.3 |\n",
+ "| LossQ | 219 |\n",
+ "| Time | 2.95e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 102 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -120 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -490 |\n",
+ "| StdTestEpRet | 223 |\n",
+ "| MaxTestEpRet | -73.4 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.24e+05 |\n",
+ "| AverageQ1Vals | -81.2 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 96.8 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -81.2 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 71.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 74.2 |\n",
+ "| LossQ | 213 |\n",
+ "| Time | 2.99e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 103 |\n",
+ "| AverageEpRet | -617 |\n",
+ "| StdEpRet | 327 |\n",
+ "| MaxEpRet | -104 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 214 |\n",
+ "| MaxTestEpRet | -248 |\n",
+ "| MinTestEpRet | -906 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.27e+05 |\n",
+ "| AverageQ1Vals | -80.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 70.5 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -80.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 41 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 74.1 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 104 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -65.7 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -425 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -774 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.29e+05 |\n",
+ "| AverageQ1Vals | -79.9 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 41.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -79.9 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 44.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 73.2 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 105 |\n",
+ "| AverageEpRet | -469 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -144 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -317 |\n",
+ "| StdTestEpRet | 137 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -541 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.31e+05 |\n",
+ "| AverageQ1Vals | -78.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 24.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -78.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 38.7 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.6 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 106 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 244 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -989 |\n",
+ "| AverageTestEpRet | -542 |\n",
+ "| StdTestEpRet | 277 |\n",
+ "| MaxTestEpRet | -129 |\n",
+ "| MinTestEpRet | -965 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.33e+05 |\n",
+ "| AverageQ1Vals | -79.1 |\n",
+ "| StdQ1Vals | 136 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -79.1 |\n",
+ "| StdQ2Vals | 136 |\n",
+ "| MaxQ2Vals | 54 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 72.7 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 107 |\n",
+ "| AverageEpRet | -571 |\n",
+ "| StdEpRet | 245 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -538 |\n",
+ "| StdTestEpRet | 189 |\n",
+ "| MaxTestEpRet | -255 |\n",
+ "| MinTestEpRet | -880 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.35e+05 |\n",
+ "| AverageQ1Vals | -78.4 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 33.8 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -78.4 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 71.6 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.15e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 108 |\n",
+ "| AverageEpRet | -481 |\n",
+ "| StdEpRet | 187 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -837 |\n",
+ "| AverageTestEpRet | -399 |\n",
+ "| StdTestEpRet | 162 |\n",
+ "| MaxTestEpRet | -149 |\n",
+ "| MinTestEpRet | -668 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.38e+05 |\n",
+ "| AverageQ1Vals | -78 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 34 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -78 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.1 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.18e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 109 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 272 |\n",
+ "| MaxEpRet | -63.6 |\n",
+ "| MinEpRet | -1.04e+03 |\n",
+ "| AverageTestEpRet | -531 |\n",
+ "| StdTestEpRet | 326 |\n",
+ "| MaxTestEpRet | -90.1 |\n",
+ "| MinTestEpRet | -1.19e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.4e+05 |\n",
+ "| AverageQ1Vals | -76.9 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 59 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.9 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 42.2 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.7 |\n",
+ "| LossQ | 212 |\n",
+ "| Time | 3.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 110 |\n",
+ "| AverageEpRet | -523 |\n",
+ "| StdEpRet | 321 |\n",
+ "| MaxEpRet | -29.2 |\n",
+ "| MinEpRet | -1.39e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 282 |\n",
+ "| MaxTestEpRet | -187 |\n",
+ "| MinTestEpRet | -1.03e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+05 |\n",
+ "| AverageQ1Vals | -76.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 43.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 53.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 70.9 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 111 |\n",
+ "| AverageEpRet | -453 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -81.5 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -524 |\n",
+ "| StdTestEpRet | 105 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -689 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.44e+05 |\n",
+ "| AverageQ1Vals | -76.5 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 30.1 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.5 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.8 |\n",
+ "| MinQ2Vals | -1.23e+03 |\n",
+ "| LossPi | 70.4 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 112 |\n",
+ "| AverageEpRet | -596 |\n",
+ "| StdEpRet | 262 |\n",
+ "| MaxEpRet | -173 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -415 |\n",
+ "| StdTestEpRet | 161 |\n",
+ "| MaxTestEpRet | -137 |\n",
+ "| MinTestEpRet | -652 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.46e+05 |\n",
+ "| AverageQ1Vals | -76.3 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 34.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.3 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 46.7 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.3 |\n",
+ "| LossQ | 198 |\n",
+ "| Time | 3.3e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 113 |\n",
+ "| AverageEpRet | -482 |\n",
+ "| StdEpRet | 333 |\n",
+ "| MaxEpRet | -62.8 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -559 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -142 |\n",
+ "| MinTestEpRet | -1.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.49e+05 |\n",
+ "| AverageQ1Vals | -76.2 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 38.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -76.2 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 50.3 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.33e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 114 |\n",
+ "| AverageEpRet | -450 |\n",
+ "| StdEpRet | 216 |\n",
+ "| MaxEpRet | -94.9 |\n",
+ "| MinEpRet | -872 |\n",
+ "| AverageTestEpRet | -432 |\n",
+ "| StdTestEpRet | 132 |\n",
+ "| MaxTestEpRet | -232 |\n",
+ "| MinTestEpRet | -645 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.51e+05 |\n",
+ "| AverageQ1Vals | -75.7 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 44.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -75.7 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 69.6 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.36e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 115 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 240 |\n",
+ "| MaxEpRet | -108 |\n",
+ "| MinEpRet | -932 |\n",
+ "| AverageTestEpRet | -371 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -143 |\n",
+ "| MinTestEpRet | -616 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.53e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 21.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 29.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 205 |\n",
+ "| Time | 3.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 116 |\n",
+ "| AverageEpRet | -538 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -960 |\n",
+ "| AverageTestEpRet | -475 |\n",
+ "| StdTestEpRet | 311 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -1.23e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.55e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 26 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 37.1 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 69.3 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.43e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 117 |\n",
+ "| AverageEpRet | -437 |\n",
+ "| StdEpRet | 203 |\n",
+ "| MaxEpRet | -92.8 |\n",
+ "| MinEpRet | -857 |\n",
+ "| AverageTestEpRet | -582 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -79.8 |\n",
+ "| MinTestEpRet | -861 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.57e+05 |\n",
+ "| AverageQ1Vals | -75.5 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 49.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -75.5 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 51 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 69.4 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.47e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 118 |\n",
+ "| AverageEpRet | -549 |\n",
+ "| StdEpRet | 236 |\n",
+ "| MaxEpRet | -111 |\n",
+ "| MinEpRet | -1.28e+03 |\n",
+ "| AverageTestEpRet | -588 |\n",
+ "| StdTestEpRet | 404 |\n",
+ "| MaxTestEpRet | -138 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.6e+05 |\n",
+ "| AverageQ1Vals | -75.1 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 31.7 |\n",
+ "| MinQ1Vals | -1.05e+03 |\n",
+ "| AverageQ2Vals | -75.1 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 38.6 |\n",
+ "| MinQ2Vals | -1.05e+03 |\n",
+ "| LossPi | 69 |\n",
+ "| LossQ | 192 |\n",
+ "| Time | 3.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 119 |\n",
+ "| AverageEpRet | -491 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -102 |\n",
+ "| MinEpRet | -945 |\n",
+ "| AverageTestEpRet | -535 |\n",
+ "| StdTestEpRet | 217 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -879 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.62e+05 |\n",
+ "| AverageQ1Vals | -74.9 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 60.9 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.9 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 28.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 69.2 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 120 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 249 |\n",
+ "| MaxEpRet | -64.6 |\n",
+ "| MinEpRet | -885 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 242 |\n",
+ "| MaxTestEpRet | -56.1 |\n",
+ "| MinTestEpRet | -838 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+05 |\n",
+ "| AverageQ1Vals | -74.8 |\n",
+ "| StdQ1Vals | 129 |\n",
+ "| MaxQ1Vals | 105 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.8 |\n",
+ "| StdQ2Vals | 129 |\n",
+ "| MaxQ2Vals | 77.4 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 69.1 |\n",
+ "| LossQ | 197 |\n",
+ "| Time | 3.56e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ " parser = argparse.ArgumentParser()\n",
+ " parser.add_argument('--cpu', type=int, default=4)\n",
+ " parser.add_argument('--num_runs', type=int, default=3)\n",
+ " args = parser.parse_args()\n",
+ "\n",
+ " eg = ExperimentGrid(name='ppo-pyt-bench')\n",
+ " eg.add('env_name', 'CartPole-v0', '', True)\n",
+ " eg.add('seed', [10*i for i in range(args.num_runs)])\n",
+ " eg.add('epochs', 10)\n",
+ " eg.add('steps_per_epoch', 4000)\n",
+ " eg.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')\n",
+ " eg.add('ac_kwargs:activation', [torch.nn.Tanh, torch.nn.ReLU], '')\n",
+ " eg.run(ppo_pytorch, num_cpu=args.cpu)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Gn3Gp40bcOVz"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 972,
+ "status": "ok",
+ "timestamp": 1584036455886,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "6GyY0wE-QBOj",
+ "outputId": "9a5e9011-e024-4a65-8383-83c062cdcad9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-900.18321242575\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "env = GyroscopeEnv()\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = torch.load('model/pyt_save/model.pt')\n",
+ "\n",
+ "# Test parameters\n",
+ "x1,x2,x3,x4,x1_ref,x3_ref,w = 0,1,0,1,1,3,25\n",
+ "state = env.reset(np.array([x1,x2,x3,x4,x1_ref,x3_ref,w]))\n",
+ "val = []\n",
+ "act = []\n",
+ "dt = 0.01\n",
+ "time = np.arange(0, 4, dt)\n",
+ "score = 0\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " action = agent.act(torch.as_tensor(state, dtype=torch.float32))\n",
+ " act.append(action)\n",
+ " state, reward, done, _ = env.step(action)\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ "\n",
+ "env.close()\n",
+ "print(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "vvMjuRHDcfrE"
+ },
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 1856,
+ "status": "ok",
+ "timestamp": 1584036457424,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "aCZCqujgcMVA",
+ "outputId": "05490294-aab8-4933-ca9b-e13ba85ecf6d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2160x2160 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(4,2,figsize=(30,30))\n",
+ "plt.subplot(4,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "plt.plot(time, [row[4] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,2)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "plt.plot(time, [row[5] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,3)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[0]- row[4]) for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[2]- row[5]) for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,7)\n",
+ "plt.title('Red gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u1 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in act],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,8)\n",
+ "plt.title('Blue gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u2 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in act],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fz9r-gaStzpk"
+ },
+ "source": [
+ "## 3D rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "HQM1E8JYc-cC"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 400\n",
+ "\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "gyroscope_ddpg_testing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/training_spinuplib/.ipynb_checkpoints/gyroscope_training_spinuplib-checkpoint.ipynb b/code/training_spinuplib/.ipynb_checkpoints/gyroscope_training_spinuplib-checkpoint.ipynb
new file mode 100644
index 0000000..56b6d87
--- /dev/null
+++ b/code/training_spinuplib/.ipynb_checkpoints/gyroscope_training_spinuplib-checkpoint.ipynb
@@ -0,0 +1,3800 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "x83dMPapQBN6"
+ },
+ "source": [
+ "# Gyroscope DDPG/TD3/SAC training (spinup library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8inmkEG5QHqx"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 655,
+ "status": "ok",
+ "timestamp": 1584030129316,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "DnnJGcnsQvZC",
+ "outputId": "9d6d1408-da29-44ed-af74-4ebdc2bd865a"
+ },
+ "outputs": [],
+ "source": [
+ "cd drive/My\\ Drive/Colab Notebooks/DDPG_uda_v0/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "fuJhdd479TpP"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'spinup'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-128c06fd51b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mvpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mspinup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'spinup'"
+ ]
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from collections import deque\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "from vpython import *\n",
+ "\n",
+ "import spinup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "O0O0t5ZR9Tp6"
+ },
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Al_k1rtvQBOM"
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self):\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = 0.05\n",
+ " \n",
+ " # Error\n",
+ " self.int_diff_x1 = 0\n",
+ " self.int_diff_x3 = 0\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 100 * 2 * np.pi / 60\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiskSpeed = 300 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxAngle,self.maxAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, x1_ref, x3_ref, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " # Angle error\n",
+ " diff_x1 = angle_normalize(x1 - x1_ref)\n",
+ " diff_x3 = angle_normalize(x3 - x3_ref)\n",
+ " \n",
+ " # Integral of error\n",
+ " self.int_diff_x1 = self.int_diff_x1 + diff_x1\n",
+ " self.int_diff_x3 = self.int_diff_x3 + diff_x3\n",
+ " \n",
+ " # Reward 1: differentiable reward (LQR obj function)\n",
+ " reward = -((3*diff_x1)**2 + (3*diff_x3)**2 + (.2*x2)**2 + (.2*x4)**2 + (.1*u1)**2 + (.1*u2)**2)\\\n",
+ " #-(0.01*abs(self.int_diff_x1) + 0.01*abs(self.int_diff_x3))\n",
+ "\n",
+ " \"\"\"# Count time spent in goal:\n",
+ " if abs(diff_x1)<0.05 and abs(diff_x3)<0.05:\n",
+ " self.countGoal +=1\n",
+ " else:\n",
+ " self.countGoal = 0\n",
+ " \n",
+ " # Reward 2: sparse reward for staying in goal range for a long time \n",
+ " if self.countGoal >= (self.timeGoal)/self.dt: #max expected reward over length becomes 0 + (totaltime-goaltime)\n",
+ " reward += 1\"\"\"\n",
+ "\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,x1_ref, x3_ref,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, state = None):\n",
+ " \n",
+ " \n",
+ " # Generate random state (for training) or use given state (for simulation)\n",
+ " if state is None:\n",
+ " self.state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " self.state = state\n",
+ "\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = x[-1]\n",
+ "\n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "be0wYIeBQBOc"
+ },
+ "source": [
+ "## Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 533
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 654004,
+ "status": "error",
+ "timestamp": 1584037207187,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "fLyFHs0yQBOd",
+ "outputId": "260489ff-5e40-416a-e529-5a0cfcaefceb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Log dir model already exists! Storing info there anyway.\n",
+ "\u001b[32;1mLogging data to model/progress.txt\u001b[0m\n",
+ "\u001b[36;1mSaving config:\n",
+ "\u001b[0m\n",
+ "{\n",
+ " \"ac_kwargs\":\t{},\n",
+ " \"act_noise\":\t0.1,\n",
+ " \"actor_critic\":\t\"MLPActorCritic\",\n",
+ " \"batch_size\":\t100,\n",
+ " \"env_fn\":\t\"GyroscopeEnv\",\n",
+ " \"epochs\":\t120,\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"gamma\":\t0.95,\n",
+ " \"logger\":\t{\n",
+ " \"<spinup.utils.logx.EpochLogger object at 0x7f7e800ca588>\":\t{\n",
+ " \"epoch_dict\":\t{},\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"first_row\":\ttrue,\n",
+ " \"log_current_row\":\t{},\n",
+ " \"log_headers\":\t[],\n",
+ " \"output_dir\":\t\"model\",\n",
+ " \"output_file\":\t{\n",
+ " \"<_io.TextIOWrapper name='model/progress.txt' mode='w' encoding='UTF-8'>\":\t{\n",
+ " \"mode\":\t\"w\"\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"logger_kwargs\":\t{\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"output_dir\":\t\"model\"\n",
+ " },\n",
+ " \"max_ep_len\":\t110,\n",
+ " \"noise_clip\":\t0.5,\n",
+ " \"num_test_episodes\":\t10,\n",
+ " \"pi_lr\":\t0.001,\n",
+ " \"policy_delay\":\t2,\n",
+ " \"polyak\":\t0.995,\n",
+ " \"q_lr\":\t0.001,\n",
+ " \"replay_size\":\t1000000,\n",
+ " \"save_freq\":\t1,\n",
+ " \"seed\":\t0,\n",
+ " \"start_steps\":\t20000,\n",
+ " \"steps_per_epoch\":\t2200,\n",
+ " \"target_noise\":\t0.2,\n",
+ " \"update_after\":\t800,\n",
+ " \"update_every\":\t50\n",
+ "}\n",
+ "\u001b[32;1m\n",
+ "Number of parameters: \t pi: 68354, \t q1: 68609, \t q2: 68609\n",
+ "\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 1 |\n",
+ "| AverageEpRet | -6.44e+03 |\n",
+ "| StdEpRet | 1.49e+03 |\n",
+ "| MaxEpRet | -4.55e+03 |\n",
+ "| MinEpRet | -9.52e+03 |\n",
+ "| AverageTestEpRet | -7.13e+03 |\n",
+ "| StdTestEpRet | 1.25e+03 |\n",
+ "| MaxTestEpRet | -4.01e+03 |\n",
+ "| MinTestEpRet | -8.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+03 |\n",
+ "| AverageQ1Vals | -123 |\n",
+ "| StdQ1Vals | 54.5 |\n",
+ "| MaxQ1Vals | 0.0809 |\n",
+ "| MinQ1Vals | -364 |\n",
+ "| AverageQ2Vals | -123 |\n",
+ "| StdQ2Vals | 54.6 |\n",
+ "| MaxQ2Vals | 1.77 |\n",
+ "| MinQ2Vals | -373 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 1.95e+03 |\n",
+ "| Time | 15.8 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 2 |\n",
+ "| AverageEpRet | -6.72e+03 |\n",
+ "| StdEpRet | 1.81e+03 |\n",
+ "| MaxEpRet | -4.58e+03 |\n",
+ "| MinEpRet | -1.07e+04 |\n",
+ "| AverageTestEpRet | -8.21e+03 |\n",
+ "| StdTestEpRet | 1.1e+03 |\n",
+ "| MaxTestEpRet | -6.46e+03 |\n",
+ "| MinTestEpRet | -1.01e+04 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+03 |\n",
+ "| AverageQ1Vals | -276 |\n",
+ "| StdQ1Vals | 98.9 |\n",
+ "| MaxQ1Vals | -54.4 |\n",
+ "| MinQ1Vals | -672 |\n",
+ "| AverageQ2Vals | -276 |\n",
+ "| StdQ2Vals | 99.2 |\n",
+ "| MaxQ2Vals | -51.6 |\n",
+ "| MinQ2Vals | -686 |\n",
+ "| LossPi | 256 |\n",
+ "| LossQ | 2.12e+03 |\n",
+ "| Time | 42.1 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 3 |\n",
+ "| AverageEpRet | -7.08e+03 |\n",
+ "| StdEpRet | 1.22e+03 |\n",
+ "| MaxEpRet | -4.94e+03 |\n",
+ "| MinEpRet | -9.27e+03 |\n",
+ "| AverageTestEpRet | -5.91e+03 |\n",
+ "| StdTestEpRet | 1.83e+03 |\n",
+ "| MaxTestEpRet | -3.79e+03 |\n",
+ "| MinTestEpRet | -9.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+03 |\n",
+ "| AverageQ1Vals | -414 |\n",
+ "| StdQ1Vals | 128 |\n",
+ "| MaxQ1Vals | -36.3 |\n",
+ "| MinQ1Vals | -952 |\n",
+ "| AverageQ2Vals | -414 |\n",
+ "| StdQ2Vals | 128 |\n",
+ "| MaxQ2Vals | -26.8 |\n",
+ "| MinQ2Vals | -950 |\n",
+ "| LossPi | 382 |\n",
+ "| LossQ | 2.76e+03 |\n",
+ "| Time | 67.9 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 4 |\n",
+ "| AverageEpRet | -7.06e+03 |\n",
+ "| StdEpRet | 1.53e+03 |\n",
+ "| MaxEpRet | -4.98e+03 |\n",
+ "| MinEpRet | -1.09e+04 |\n",
+ "| AverageTestEpRet | -5.01e+03 |\n",
+ "| StdTestEpRet | 1.18e+03 |\n",
+ "| MaxTestEpRet | -3.34e+03 |\n",
+ "| MinTestEpRet | -6.44e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+03 |\n",
+ "| AverageQ1Vals | -501 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 11.2 |\n",
+ "| MinQ1Vals | -1.07e+03 |\n",
+ "| AverageQ2Vals | -501 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 16.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 459 |\n",
+ "| LossQ | 3.94e+03 |\n",
+ "| Time | 92 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 5 |\n",
+ "| AverageEpRet | -6.3e+03 |\n",
+ "| StdEpRet | 1.14e+03 |\n",
+ "| MaxEpRet | -3.87e+03 |\n",
+ "| MinEpRet | -8.1e+03 |\n",
+ "| AverageTestEpRet | -4.27e+03 |\n",
+ "| StdTestEpRet | 1.52e+03 |\n",
+ "| MaxTestEpRet | -1.82e+03 |\n",
+ "| MinTestEpRet | -6.62e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+04 |\n",
+ "| AverageQ1Vals | -552 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | -15.7 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -552 |\n",
+ "| StdQ2Vals | 172 |\n",
+ "| MaxQ2Vals | 17.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 508 |\n",
+ "| LossQ | 4.63e+03 |\n",
+ "| Time | 116 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 6 |\n",
+ "| AverageEpRet | -6.76e+03 |\n",
+ "| StdEpRet | 1.35e+03 |\n",
+ "| MaxEpRet | -4.61e+03 |\n",
+ "| MinEpRet | -1.13e+04 |\n",
+ "| AverageTestEpRet | -3.67e+03 |\n",
+ "| StdTestEpRet | 994 |\n",
+ "| MaxTestEpRet | -2.24e+03 |\n",
+ "| MinTestEpRet | -5.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+04 |\n",
+ "| AverageQ1Vals | -574 |\n",
+ "| StdQ1Vals | 186 |\n",
+ "| MaxQ1Vals | -39.4 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -574 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | -27.9 |\n",
+ "| MinQ2Vals | -1.29e+03 |\n",
+ "| LossPi | 527 |\n",
+ "| LossQ | 5.18e+03 |\n",
+ "| Time | 139 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 7 |\n",
+ "| AverageEpRet | -7.02e+03 |\n",
+ "| StdEpRet | 1.79e+03 |\n",
+ "| MaxEpRet | -3.34e+03 |\n",
+ "| MinEpRet | -9.5e+03 |\n",
+ "| AverageTestEpRet | -3.05e+03 |\n",
+ "| StdTestEpRet | 1.56e+03 |\n",
+ "| MaxTestEpRet | -1.34e+03 |\n",
+ "| MinTestEpRet | -6.7e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+04 |\n",
+ "| AverageQ1Vals | -582 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | -68.9 |\n",
+ "| MinQ1Vals | -1.38e+03 |\n",
+ "| AverageQ2Vals | -582 |\n",
+ "| StdQ2Vals | 195 |\n",
+ "| MaxQ2Vals | -43.9 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 534 |\n",
+ "| LossQ | 5.26e+03 |\n",
+ "| Time | 163 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 8 |\n",
+ "| AverageEpRet | -6.62e+03 |\n",
+ "| StdEpRet | 1.42e+03 |\n",
+ "| MaxEpRet | -4.04e+03 |\n",
+ "| MinEpRet | -9.02e+03 |\n",
+ "| AverageTestEpRet | -1.54e+03 |\n",
+ "| StdTestEpRet | 771 |\n",
+ "| MaxTestEpRet | -595 |\n",
+ "| MinTestEpRet | -3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+04 |\n",
+ "| AverageQ1Vals | -573 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | -4.68 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -573 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 22.9 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 522 |\n",
+ "| LossQ | 5.51e+03 |\n",
+ "| Time | 187 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 9 |\n",
+ "| AverageEpRet | -6.7e+03 |\n",
+ "| StdEpRet | 1.58e+03 |\n",
+ "| MaxEpRet | -4.21e+03 |\n",
+ "| MinEpRet | -9.46e+03 |\n",
+ "| AverageTestEpRet | -842 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -411 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+04 |\n",
+ "| AverageQ1Vals | -546 |\n",
+ "| StdQ1Vals | 223 |\n",
+ "| MaxQ1Vals | 71.2 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -546 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 60.5 |\n",
+ "| MinQ2Vals | -1.41e+03 |\n",
+ "| LossPi | 490 |\n",
+ "| LossQ | 5.83e+03 |\n",
+ "| Time | 211 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 10 |\n",
+ "| AverageEpRet | -1.66e+03 |\n",
+ "| StdEpRet | 1.99e+03 |\n",
+ "| MaxEpRet | -153 |\n",
+ "| MinEpRet | -8.64e+03 |\n",
+ "| AverageTestEpRet | -872 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -487 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+04 |\n",
+ "| AverageQ1Vals | -488 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 151 |\n",
+ "| MinQ1Vals | -1.43e+03 |\n",
+ "| AverageQ2Vals | -488 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 127 |\n",
+ "| MinQ2Vals | -1.4e+03 |\n",
+ "| LossPi | 432 |\n",
+ "| LossQ | 5.85e+03 |\n",
+ "| Time | 239 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 11 |\n",
+ "| AverageEpRet | -947 |\n",
+ "| StdEpRet | 493 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.89e+03 |\n",
+ "| AverageTestEpRet | -1.09e+03 |\n",
+ "| StdTestEpRet | 661 |\n",
+ "| MaxTestEpRet | -271 |\n",
+ "| MinTestEpRet | -2.78e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+04 |\n",
+ "| AverageQ1Vals | -425 |\n",
+ "| StdQ1Vals | 258 |\n",
+ "| MaxQ1Vals | 141 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -425 |\n",
+ "| StdQ2Vals | 258 |\n",
+ "| MaxQ2Vals | 137 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 376 |\n",
+ "| LossQ | 4.71e+03 |\n",
+ "| Time | 263 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 12 |\n",
+ "| AverageEpRet | -888 |\n",
+ "| StdEpRet | 472 |\n",
+ "| MaxEpRet | -325 |\n",
+ "| MinEpRet | -2.33e+03 |\n",
+ "| AverageTestEpRet | -602 |\n",
+ "| StdTestEpRet | 199 |\n",
+ "| MaxTestEpRet | -286 |\n",
+ "| MinTestEpRet | -910 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+04 |\n",
+ "| AverageQ1Vals | -384 |\n",
+ "| StdQ1Vals | 264 |\n",
+ "| MaxQ1Vals | 161 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -384 |\n",
+ "| StdQ2Vals | 264 |\n",
+ "| MaxQ2Vals | 142 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 341 |\n",
+ "| LossQ | 3.72e+03 |\n",
+ "| Time | 288 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 13 |\n",
+ "| AverageEpRet | -797 |\n",
+ "| StdEpRet | 536 |\n",
+ "| MaxEpRet | -48.6 |\n",
+ "| MinEpRet | -2.46e+03 |\n",
+ "| AverageTestEpRet | -857 |\n",
+ "| StdTestEpRet | 354 |\n",
+ "| MaxTestEpRet | -467 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.86e+04 |\n",
+ "| AverageQ1Vals | -354 |\n",
+ "| StdQ1Vals | 270 |\n",
+ "| MaxQ1Vals | 169 |\n",
+ "| MinQ1Vals | -1.36e+03 |\n",
+ "| AverageQ2Vals | -354 |\n",
+ "| StdQ2Vals | 270 |\n",
+ "| MaxQ2Vals | 166 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 314 |\n",
+ "| LossQ | 3.07e+03 |\n",
+ "| Time | 312 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 14 |\n",
+ "| AverageEpRet | -926 |\n",
+ "| StdEpRet | 418 |\n",
+ "| MaxEpRet | -333 |\n",
+ "| MinEpRet | -1.67e+03 |\n",
+ "| AverageTestEpRet | -902 |\n",
+ "| StdTestEpRet | 481 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -2.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.08e+04 |\n",
+ "| AverageQ1Vals | -326 |\n",
+ "| StdQ1Vals | 274 |\n",
+ "| MaxQ1Vals | 192 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -326 |\n",
+ "| StdQ2Vals | 274 |\n",
+ "| MaxQ2Vals | 191 |\n",
+ "| MinQ2Vals | -1.34e+03 |\n",
+ "| LossPi | 288 |\n",
+ "| LossQ | 2.66e+03 |\n",
+ "| Time | 337 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 15 |\n",
+ "| AverageEpRet | -738 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -267 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -1.45e+03 |\n",
+ "| StdTestEpRet | 1.42e+03 |\n",
+ "| MaxTestEpRet | -185 |\n",
+ "| MinTestEpRet | -4.34e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.3e+04 |\n",
+ "| AverageQ1Vals | -297 |\n",
+ "| StdQ1Vals | 279 |\n",
+ "| MaxQ1Vals | 220 |\n",
+ "| MinQ1Vals | -1.31e+03 |\n",
+ "| AverageQ2Vals | -297 |\n",
+ "| StdQ2Vals | 279 |\n",
+ "| MaxQ2Vals | 221 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 261 |\n",
+ "| LossQ | 2.37e+03 |\n",
+ "| Time | 362 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 16 |\n",
+ "| AverageEpRet | -1.37e+03 |\n",
+ "| StdEpRet | 1.85e+03 |\n",
+ "| MaxEpRet | -223 |\n",
+ "| MinEpRet | -8.63e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 159 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -913 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.52e+04 |\n",
+ "| AverageQ1Vals | -267 |\n",
+ "| StdQ1Vals | 277 |\n",
+ "| MaxQ1Vals | 279 |\n",
+ "| MinQ1Vals | -1.3e+03 |\n",
+ "| AverageQ2Vals | -267 |\n",
+ "| StdQ2Vals | 277 |\n",
+ "| MaxQ2Vals | 269 |\n",
+ "| MinQ2Vals | -1.27e+03 |\n",
+ "| LossPi | 231 |\n",
+ "| LossQ | 2.21e+03 |\n",
+ "| Time | 386 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 17 |\n",
+ "| AverageEpRet | -754 |\n",
+ "| StdEpRet | 595 |\n",
+ "| MaxEpRet | -202 |\n",
+ "| MinEpRet | -2.5e+03 |\n",
+ "| AverageTestEpRet | -636 |\n",
+ "| StdTestEpRet | 389 |\n",
+ "| MaxTestEpRet | -147 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.74e+04 |\n",
+ "| AverageQ1Vals | -245 |\n",
+ "| StdQ1Vals | 273 |\n",
+ "| MaxQ1Vals | 278 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -245 |\n",
+ "| StdQ2Vals | 273 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 211 |\n",
+ "| LossQ | 2.03e+03 |\n",
+ "| Time | 411 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 18 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -241 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -713 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -419 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.96e+04 |\n",
+ "| AverageQ1Vals | -226 |\n",
+ "| StdQ1Vals | 268 |\n",
+ "| MaxQ1Vals | 275 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -226 |\n",
+ "| StdQ2Vals | 268 |\n",
+ "| MaxQ2Vals | 265 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 194 |\n",
+ "| LossQ | 1.83e+03 |\n",
+ "| Time | 434 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 19 |\n",
+ "| AverageEpRet | -745 |\n",
+ "| StdEpRet | 537 |\n",
+ "| MaxEpRet | -109 |\n",
+ "| MinEpRet | -2.14e+03 |\n",
+ "| AverageTestEpRet | -963 |\n",
+ "| StdTestEpRet | 641 |\n",
+ "| MaxTestEpRet | -159 |\n",
+ "| MinTestEpRet | -2.65e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.18e+04 |\n",
+ "| AverageQ1Vals | -214 |\n",
+ "| StdQ1Vals | 263 |\n",
+ "| MaxQ1Vals | 273 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -214 |\n",
+ "| StdQ2Vals | 263 |\n",
+ "| MaxQ2Vals | 266 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 183 |\n",
+ "| LossQ | 1.67e+03 |\n",
+ "| Time | 458 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 20 |\n",
+ "| AverageEpRet | -711 |\n",
+ "| StdEpRet | 511 |\n",
+ "| MaxEpRet | -18.3 |\n",
+ "| MinEpRet | -1.83e+03 |\n",
+ "| AverageTestEpRet | -803 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -126 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+04 |\n",
+ "| AverageQ1Vals | -198 |\n",
+ "| StdQ1Vals | 260 |\n",
+ "| MaxQ1Vals | 276 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -198 |\n",
+ "| StdQ2Vals | 260 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 169 |\n",
+ "| LossQ | 1.57e+03 |\n",
+ "| Time | 483 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 21 |\n",
+ "| AverageEpRet | -664 |\n",
+ "| StdEpRet | 403 |\n",
+ "| MaxEpRet | -176 |\n",
+ "| MinEpRet | -1.87e+03 |\n",
+ "| AverageTestEpRet | -577 |\n",
+ "| StdTestEpRet | 343 |\n",
+ "| MaxTestEpRet | -133 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.62e+04 |\n",
+ "| AverageQ1Vals | -187 |\n",
+ "| StdQ1Vals | 254 |\n",
+ "| MaxQ1Vals | 269 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -187 |\n",
+ "| StdQ2Vals | 254 |\n",
+ "| MaxQ2Vals | 262 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 159 |\n",
+ "| LossQ | 1.48e+03 |\n",
+ "| Time | 507 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 22 |\n",
+ "| AverageEpRet | -662 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -261 |\n",
+ "| MinEpRet | -1.64e+03 |\n",
+ "| AverageTestEpRet | -780 |\n",
+ "| StdTestEpRet | 458 |\n",
+ "| MaxTestEpRet | -39.9 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.84e+04 |\n",
+ "| AverageQ1Vals | -178 |\n",
+ "| StdQ1Vals | 249 |\n",
+ "| MaxQ1Vals | 260 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -178 |\n",
+ "| StdQ2Vals | 249 |\n",
+ "| MaxQ2Vals | 258 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 151 |\n",
+ "| LossQ | 1.35e+03 |\n",
+ "| Time | 531 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 23 |\n",
+ "| AverageEpRet | -524 |\n",
+ "| StdEpRet | 312 |\n",
+ "| MaxEpRet | -85.9 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -768 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -329 |\n",
+ "| MinTestEpRet | -1.3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.06e+04 |\n",
+ "| AverageQ1Vals | -171 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 239 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -170 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 246 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 145 |\n",
+ "| LossQ | 1.29e+03 |\n",
+ "| Time | 555 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 24 |\n",
+ "| AverageEpRet | -714 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -181 |\n",
+ "| MinEpRet | -1.29e+03 |\n",
+ "| AverageTestEpRet | -433 |\n",
+ "| StdTestEpRet | 184 |\n",
+ "| MaxTestEpRet | -178 |\n",
+ "| MinTestEpRet | -786 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.28e+04 |\n",
+ "| AverageQ1Vals | -164 |\n",
+ "| StdQ1Vals | 238 |\n",
+ "| MaxQ1Vals | 227 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -164 |\n",
+ "| StdQ2Vals | 238 |\n",
+ "| MaxQ2Vals | 229 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 139 |\n",
+ "| LossQ | 1.22e+03 |\n",
+ "| Time | 579 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 25 |\n",
+ "| AverageEpRet | -709 |\n",
+ "| StdEpRet | 425 |\n",
+ "| MaxEpRet | -88.8 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -808 |\n",
+ "| StdTestEpRet | 476 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.81e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.5e+04 |\n",
+ "| AverageQ1Vals | -159 |\n",
+ "| StdQ1Vals | 233 |\n",
+ "| MaxQ1Vals | 209 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -159 |\n",
+ "| StdQ2Vals | 233 |\n",
+ "| MaxQ2Vals | 209 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.15e+03 |\n",
+ "| Time | 603 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 26 |\n",
+ "| AverageEpRet | -813 |\n",
+ "| StdEpRet | 433 |\n",
+ "| MaxEpRet | -247 |\n",
+ "| MinEpRet | -1.81e+03 |\n",
+ "| AverageTestEpRet | -702 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.72e+04 |\n",
+ "| AverageQ1Vals | -158 |\n",
+ "| StdQ1Vals | 231 |\n",
+ "| MaxQ1Vals | 194 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -158 |\n",
+ "| StdQ2Vals | 231 |\n",
+ "| MaxQ2Vals | 198 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.14e+03 |\n",
+ "| Time | 628 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 27 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 282 |\n",
+ "| MaxEpRet | -336 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -934 |\n",
+ "| StdTestEpRet | 626 |\n",
+ "| MaxTestEpRet | -317 |\n",
+ "| MinTestEpRet | -2.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.94e+04 |\n",
+ "| AverageQ1Vals | -157 |\n",
+ "| StdQ1Vals | 229 |\n",
+ "| MaxQ1Vals | 190 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -157 |\n",
+ "| StdQ2Vals | 229 |\n",
+ "| MaxQ2Vals | 192 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 135 |\n",
+ "| LossQ | 1.03e+03 |\n",
+ "| Time | 653 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 28 |\n",
+ "| AverageEpRet | -965 |\n",
+ "| StdEpRet | 522 |\n",
+ "| MaxEpRet | -172 |\n",
+ "| MinEpRet | -2.3e+03 |\n",
+ "| AverageTestEpRet | -599 |\n",
+ "| StdTestEpRet | 398 |\n",
+ "| MaxTestEpRet | -163 |\n",
+ "| MinTestEpRet | -1.32e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.16e+04 |\n",
+ "| AverageQ1Vals | -154 |\n",
+ "| StdQ1Vals | 226 |\n",
+ "| MaxQ1Vals | 176 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -154 |\n",
+ "| StdQ2Vals | 226 |\n",
+ "| MaxQ2Vals | 188 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 973 |\n",
+ "| Time | 677 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 29 |\n",
+ "| AverageEpRet | -766 |\n",
+ "| StdEpRet | 367 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.69e+03 |\n",
+ "| AverageTestEpRet | -659 |\n",
+ "| StdTestEpRet | 229 |\n",
+ "| MaxTestEpRet | -303 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.38e+04 |\n",
+ "| AverageQ1Vals | -153 |\n",
+ "| StdQ1Vals | 225 |\n",
+ "| MaxQ1Vals | 173 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -153 |\n",
+ "| StdQ2Vals | 225 |\n",
+ "| MaxQ2Vals | 176 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 961 |\n",
+ "| Time | 702 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 30 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -388 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -686 |\n",
+ "| StdTestEpRet | 188 |\n",
+ "| MaxTestEpRet | -312 |\n",
+ "| MinTestEpRet | -977 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+04 |\n",
+ "| AverageQ1Vals | -151 |\n",
+ "| StdQ1Vals | 222 |\n",
+ "| MaxQ1Vals | 162 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -151 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 159 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 131 |\n",
+ "| LossQ | 913 |\n",
+ "| Time | 727 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 31 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 442 |\n",
+ "| MaxEpRet | -163 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -678 |\n",
+ "| StdTestEpRet | 371 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.82e+04 |\n",
+ "| AverageQ1Vals | -149 |\n",
+ "| StdQ1Vals | 219 |\n",
+ "| MaxQ1Vals | 149 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -149 |\n",
+ "| StdQ2Vals | 219 |\n",
+ "| MaxQ2Vals | 153 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 855 |\n",
+ "| Time | 753 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 32 |\n",
+ "| AverageEpRet | -760 |\n",
+ "| StdEpRet | 313 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -1.49e+03 |\n",
+ "| AverageTestEpRet | -759 |\n",
+ "| StdTestEpRet | 430 |\n",
+ "| MaxTestEpRet | -256 |\n",
+ "| MinTestEpRet | -1.64e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.04e+04 |\n",
+ "| AverageQ1Vals | -150 |\n",
+ "| StdQ1Vals | 217 |\n",
+ "| MaxQ1Vals | 146 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -150 |\n",
+ "| StdQ2Vals | 217 |\n",
+ "| MaxQ2Vals | 145 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 835 |\n",
+ "| Time | 775 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 33 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 395 |\n",
+ "| MaxEpRet | -57.1 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 368 |\n",
+ "| MaxTestEpRet | -120 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.26e+04 |\n",
+ "| AverageQ1Vals | -147 |\n",
+ "| StdQ1Vals | 213 |\n",
+ "| MaxQ1Vals | 130 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -147 |\n",
+ "| StdQ2Vals | 213 |\n",
+ "| MaxQ2Vals | 125 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 129 |\n",
+ "| LossQ | 800 |\n",
+ "| Time | 796 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 34 |\n",
+ "| AverageEpRet | -732 |\n",
+ "| StdEpRet | 361 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.6e+03 |\n",
+ "| AverageTestEpRet | -737 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -278 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.48e+04 |\n",
+ "| AverageQ1Vals | -144 |\n",
+ "| StdQ1Vals | 211 |\n",
+ "| MaxQ1Vals | 133 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -144 |\n",
+ "| StdQ2Vals | 211 |\n",
+ "| MaxQ2Vals | 121 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 126 |\n",
+ "| LossQ | 784 |\n",
+ "| Time | 820 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 35 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -159 |\n",
+ "| MinEpRet | -1.58e+03 |\n",
+ "| AverageTestEpRet | -766 |\n",
+ "| StdTestEpRet | 358 |\n",
+ "| MaxTestEpRet | -85.9 |\n",
+ "| MinTestEpRet | -1.35e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.7e+04 |\n",
+ "| AverageQ1Vals | -143 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -143 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 107 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 730 |\n",
+ "| Time | 846 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 36 |\n",
+ "| AverageEpRet | -848 |\n",
+ "| StdEpRet | 733 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -3.32e+03 |\n",
+ "| AverageTestEpRet | -765 |\n",
+ "| StdTestEpRet | 340 |\n",
+ "| MaxTestEpRet | -191 |\n",
+ "| MinTestEpRet | -1.48e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.92e+04 |\n",
+ "| AverageQ1Vals | -141 |\n",
+ "| StdQ1Vals | 207 |\n",
+ "| MaxQ1Vals | 115 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -141 |\n",
+ "| StdQ2Vals | 207 |\n",
+ "| MaxQ2Vals | 108 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 703 |\n",
+ "| Time | 873 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 37 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 379 |\n",
+ "| MaxEpRet | -11.3 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -646 |\n",
+ "| StdTestEpRet | 399 |\n",
+ "| MaxTestEpRet | -102 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.14e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 204 |\n",
+ "| MaxQ1Vals | 103 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 204 |\n",
+ "| MaxQ2Vals | 96.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 667 |\n",
+ "| Time | 900 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 38 |\n",
+ "| AverageEpRet | -634 |\n",
+ "| StdEpRet | 304 |\n",
+ "| MaxEpRet | -44.1 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -716 |\n",
+ "| StdTestEpRet | 356 |\n",
+ "| MaxTestEpRet | -263 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.36e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 203 |\n",
+ "| MaxQ1Vals | 91.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 203 |\n",
+ "| MaxQ2Vals | 86 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 664 |\n",
+ "| Time | 929 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 39 |\n",
+ "| AverageEpRet | -630 |\n",
+ "| StdEpRet | 375 |\n",
+ "| MaxEpRet | -127 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -591 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -204 |\n",
+ "| MinTestEpRet | -946 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.58e+04 |\n",
+ "| AverageQ1Vals | -139 |\n",
+ "| StdQ1Vals | 201 |\n",
+ "| MaxQ1Vals | 86.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -139 |\n",
+ "| StdQ2Vals | 201 |\n",
+ "| MaxQ2Vals | 77.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 649 |\n",
+ "| Time | 960 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 40 |\n",
+ "| AverageEpRet | -506 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -67.2 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -621 |\n",
+ "| StdTestEpRet | 351 |\n",
+ "| MaxTestEpRet | -171 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+04 |\n",
+ "| AverageQ1Vals | -137 |\n",
+ "| StdQ1Vals | 199 |\n",
+ "| MaxQ1Vals | 77.5 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -137 |\n",
+ "| StdQ2Vals | 199 |\n",
+ "| MaxQ2Vals | 66.2 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 122 |\n",
+ "| LossQ | 629 |\n",
+ "| Time | 991 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 41 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 301 |\n",
+ "| MaxEpRet | -182 |\n",
+ "| MinEpRet | -1.31e+03 |\n",
+ "| AverageTestEpRet | -784 |\n",
+ "| StdTestEpRet | 309 |\n",
+ "| MaxTestEpRet | -408 |\n",
+ "| MinTestEpRet | -1.4e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.02e+04 |\n",
+ "| AverageQ1Vals | -136 |\n",
+ "| StdQ1Vals | 197 |\n",
+ "| MaxQ1Vals | 74.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -136 |\n",
+ "| StdQ2Vals | 197 |\n",
+ "| MaxQ2Vals | 74.3 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 121 |\n",
+ "| LossQ | 587 |\n",
+ "| Time | 1.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 42 |\n",
+ "| AverageEpRet | -623 |\n",
+ "| StdEpRet | 295 |\n",
+ "| MaxEpRet | -69.3 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -806 |\n",
+ "| StdTestEpRet | 386 |\n",
+ "| MaxTestEpRet | -285 |\n",
+ "| MinTestEpRet | -1.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.24e+04 |\n",
+ "| AverageQ1Vals | -134 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | 59.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -134 |\n",
+ "| StdQ2Vals | 196 |\n",
+ "| MaxQ2Vals | 67.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 120 |\n",
+ "| LossQ | 584 |\n",
+ "| Time | 1.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 43 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 255 |\n",
+ "| MaxEpRet | -79.6 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -620 |\n",
+ "| StdTestEpRet | 300 |\n",
+ "| MaxTestEpRet | -160 |\n",
+ "| MinTestEpRet | -971 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.46e+04 |\n",
+ "| AverageQ1Vals | -133 |\n",
+ "| StdQ1Vals | 194 |\n",
+ "| MaxQ1Vals | 63.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -133 |\n",
+ "| StdQ2Vals | 194 |\n",
+ "| MaxQ2Vals | 68.2 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 561 |\n",
+ "| Time | 1.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 44 |\n",
+ "| AverageEpRet | -583 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -63.3 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -507 |\n",
+ "| StdTestEpRet | 296 |\n",
+ "| MaxTestEpRet | -107 |\n",
+ "| MinTestEpRet | -996 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.68e+04 |\n",
+ "| AverageQ1Vals | -132 |\n",
+ "| StdQ1Vals | 191 |\n",
+ "| MaxQ1Vals | 49.2 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -132 |\n",
+ "| StdQ2Vals | 191 |\n",
+ "| MaxQ2Vals | 49.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 532 |\n",
+ "| Time | 1.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 45 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 309 |\n",
+ "| MaxEpRet | -147 |\n",
+ "| MinEpRet | -1.23e+03 |\n",
+ "| AverageTestEpRet | -727 |\n",
+ "| StdTestEpRet | 348 |\n",
+ "| MaxTestEpRet | -203 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.9e+04 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 50.6 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 47.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 116 |\n",
+ "| LossQ | 517 |\n",
+ "| Time | 1.14e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 46 |\n",
+ "| AverageEpRet | -649 |\n",
+ "| StdEpRet | 325 |\n",
+ "| MaxEpRet | -151 |\n",
+ "| MinEpRet | -1.5e+03 |\n",
+ "| AverageTestEpRet | -647 |\n",
+ "| StdTestEpRet | 317 |\n",
+ "| MaxTestEpRet | -219 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.01e+05 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 90.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 115 |\n",
+ "| LossQ | 514 |\n",
+ "| Time | 1.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 47 |\n",
+ "| AverageEpRet | -444 |\n",
+ "| StdEpRet | 183 |\n",
+ "| MaxEpRet | -183 |\n",
+ "| MinEpRet | -968 |\n",
+ "| AverageTestEpRet | -669 |\n",
+ "| StdTestEpRet | 245 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.12e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.03e+05 |\n",
+ "| AverageQ1Vals | -127 |\n",
+ "| StdQ1Vals | 185 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -127 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | 55.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 113 |\n",
+ "| LossQ | 489 |\n",
+ "| Time | 1.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 48 |\n",
+ "| AverageEpRet | -673 |\n",
+ "| StdEpRet | 332 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.41e+03 |\n",
+ "| AverageTestEpRet | -534 |\n",
+ "| StdTestEpRet | 252 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.06e+05 |\n",
+ "| AverageQ1Vals | -125 |\n",
+ "| StdQ1Vals | 182 |\n",
+ "| MaxQ1Vals | 96.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -125 |\n",
+ "| StdQ2Vals | 182 |\n",
+ "| MaxQ2Vals | 93.5 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 473 |\n",
+ "| Time | 1.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 49 |\n",
+ "| AverageEpRet | -475 |\n",
+ "| StdEpRet | 243 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -572 |\n",
+ "| StdTestEpRet | 339 |\n",
+ "| MaxTestEpRet | -155 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.08e+05 |\n",
+ "| AverageQ1Vals | -124 |\n",
+ "| StdQ1Vals | 181 |\n",
+ "| MaxQ1Vals | 66.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -124 |\n",
+ "| StdQ2Vals | 181 |\n",
+ "| MaxQ2Vals | 60.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 466 |\n",
+ "| Time | 1.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 50 |\n",
+ "| AverageEpRet | -581 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -121 |\n",
+ "| MinEpRet | -1.34e+03 |\n",
+ "| AverageTestEpRet | -687 |\n",
+ "| StdTestEpRet | 246 |\n",
+ "| MaxTestEpRet | -246 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 50.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 439 |\n",
+ "| Time | 1.31e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 51 |\n",
+ "| AverageEpRet | -599 |\n",
+ "| StdEpRet | 287 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 204 |\n",
+ "| MaxTestEpRet | -216 |\n",
+ "| MinTestEpRet | -856 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.12e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 78.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 76.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 110 |\n",
+ "| LossQ | 437 |\n",
+ "| Time | 1.34e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 52 |\n",
+ "| AverageEpRet | -636 |\n",
+ "| StdEpRet | 283 |\n",
+ "| MaxEpRet | -220 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -712 |\n",
+ "| StdTestEpRet | 185 |\n",
+ "| MaxTestEpRet | -457 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.14e+05 |\n",
+ "| AverageQ1Vals | -121 |\n",
+ "| StdQ1Vals | 178 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -121 |\n",
+ "| StdQ2Vals | 178 |\n",
+ "| MaxQ2Vals | 54.7 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 431 |\n",
+ "| Time | 1.37e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 53 |\n",
+ "| AverageEpRet | -591 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -132 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -551 |\n",
+ "| StdTestEpRet | 206 |\n",
+ "| MaxTestEpRet | -333 |\n",
+ "| MinTestEpRet | -975 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.17e+05 |\n",
+ "| AverageQ1Vals | -119 |\n",
+ "| StdQ1Vals | 176 |\n",
+ "| MaxQ1Vals | 54.4 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -119 |\n",
+ "| StdQ2Vals | 176 |\n",
+ "| MaxQ2Vals | 89.1 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 108 |\n",
+ "| LossQ | 418 |\n",
+ "| Time | 1.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 54 |\n",
+ "| AverageEpRet | -426 |\n",
+ "| StdEpRet | 230 |\n",
+ "| MaxEpRet | -90.2 |\n",
+ "| MinEpRet | -906 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 197 |\n",
+ "| MaxTestEpRet | -124 |\n",
+ "| MinTestEpRet | -708 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.19e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 175 |\n",
+ "| MaxQ1Vals | 47.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 175 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 401 |\n",
+ "| Time | 1.43e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 55 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -939 |\n",
+ "| AverageTestEpRet | -615 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -400 |\n",
+ "| MinTestEpRet | -952 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.21e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 174 |\n",
+ "| MaxQ1Vals | 54.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 174 |\n",
+ "| MaxQ2Vals | 68.6 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 392 |\n",
+ "| Time | 1.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 56 |\n",
+ "| AverageEpRet | -552 |\n",
+ "| StdEpRet | 234 |\n",
+ "| MaxEpRet | -228 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -400 |\n",
+ "| StdTestEpRet | 293 |\n",
+ "| MaxTestEpRet | -66.3 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.23e+05 |\n",
+ "| AverageQ1Vals | -117 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | 66.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -117 |\n",
+ "| StdQ2Vals | 173 |\n",
+ "| MaxQ2Vals | 69.5 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 105 |\n",
+ "| LossQ | 400 |\n",
+ "| Time | 1.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 57 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 318 |\n",
+ "| MaxEpRet | -33.7 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -480 |\n",
+ "| StdTestEpRet | 210 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -802 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.25e+05 |\n",
+ "| AverageQ1Vals | -114 |\n",
+ "| StdQ1Vals | 171 |\n",
+ "| MaxQ1Vals | 68.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -114 |\n",
+ "| StdQ2Vals | 171 |\n",
+ "| MaxQ2Vals | 68.8 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 103 |\n",
+ "| LossQ | 380 |\n",
+ "| Time | 1.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 58 |\n",
+ "| AverageEpRet | -590 |\n",
+ "| StdEpRet | 261 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 234 |\n",
+ "| MaxTestEpRet | -206 |\n",
+ "| MinTestEpRet | -935 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.28e+05 |\n",
+ "| AverageQ1Vals | -113 |\n",
+ "| StdQ1Vals | 170 |\n",
+ "| MaxQ1Vals | 72.9 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -113 |\n",
+ "| StdQ2Vals | 170 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 368 |\n",
+ "| Time | 1.56e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 59 |\n",
+ "| AverageEpRet | -588 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -1.05e+03 |\n",
+ "| AverageTestEpRet | -467 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -763 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.3e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 169 |\n",
+ "| MaxQ1Vals | 64.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 169 |\n",
+ "| MaxQ2Vals | 93.3 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 367 |\n",
+ "| Time | 1.6e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 60 |\n",
+ "| AverageEpRet | -495 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -44.8 |\n",
+ "| MinEpRet | -1.71e+03 |\n",
+ "| AverageTestEpRet | -654 |\n",
+ "| StdTestEpRet | 261 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.05e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 168 |\n",
+ "| MaxQ1Vals | 68.9 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 168 |\n",
+ "| MaxQ2Vals | 73.7 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 101 |\n",
+ "| LossQ | 363 |\n",
+ "| Time | 1.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 61 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 376 |\n",
+ "| MaxEpRet | -74.9 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -574 |\n",
+ "| StdTestEpRet | 410 |\n",
+ "| MaxTestEpRet | -144 |\n",
+ "| MinTestEpRet | -1.57e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.34e+05 |\n",
+ "| AverageQ1Vals | -110 |\n",
+ "| StdQ1Vals | 166 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -110 |\n",
+ "| StdQ2Vals | 166 |\n",
+ "| MaxQ2Vals | 52.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 100 |\n",
+ "| LossQ | 349 |\n",
+ "| Time | 1.66e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 62 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -485 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -886 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.36e+05 |\n",
+ "| AverageQ1Vals | -109 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 67.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -109 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 98.7 |\n",
+ "| LossQ | 358 |\n",
+ "| Time | 1.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 63 |\n",
+ "| AverageEpRet | -448 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -56.4 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -341 |\n",
+ "| StdTestEpRet | 181 |\n",
+ "| MaxTestEpRet | -63.6 |\n",
+ "| MinTestEpRet | -608 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.39e+05 |\n",
+ "| AverageQ1Vals | -108 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -108 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 52.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 97.8 |\n",
+ "| LossQ | 344 |\n",
+ "| Time | 1.73e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 64 |\n",
+ "| AverageEpRet | -525 |\n",
+ "| StdEpRet | 226 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -532 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -302 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.41e+05 |\n",
+ "| AverageQ1Vals | -106 |\n",
+ "| StdQ1Vals | 164 |\n",
+ "| MaxQ1Vals | 50.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -106 |\n",
+ "| StdQ2Vals | 164 |\n",
+ "| MaxQ2Vals | 37.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 95.7 |\n",
+ "| LossQ | 340 |\n",
+ "| Time | 1.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 65 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -31.2 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -520 |\n",
+ "| StdTestEpRet | 359 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -1.51e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.43e+05 |\n",
+ "| AverageQ1Vals | -105 |\n",
+ "| StdQ1Vals | 162 |\n",
+ "| MaxQ1Vals | 58.3 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -105 |\n",
+ "| StdQ2Vals | 162 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 95.2 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 66 |\n",
+ "| AverageEpRet | -622 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -126 |\n",
+ "| MinEpRet | -1.33e+03 |\n",
+ "| AverageTestEpRet | -525 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.45e+05 |\n",
+ "| AverageQ1Vals | -104 |\n",
+ "| StdQ1Vals | 163 |\n",
+ "| MaxQ1Vals | 46.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -104 |\n",
+ "| StdQ2Vals | 163 |\n",
+ "| MaxQ2Vals | 73.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 94.4 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.83e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 67 |\n",
+ "| AverageEpRet | -483 |\n",
+ "| StdEpRet | 204 |\n",
+ "| MaxEpRet | -118 |\n",
+ "| MinEpRet | -836 |\n",
+ "| AverageTestEpRet | -530 |\n",
+ "| StdTestEpRet | 231 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -994 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.47e+05 |\n",
+ "| AverageQ1Vals | -103 |\n",
+ "| StdQ1Vals | 161 |\n",
+ "| MaxQ1Vals | 56 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -103 |\n",
+ "| StdQ2Vals | 161 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 93.2 |\n",
+ "| LossQ | 325 |\n",
+ "| Time | 1.86e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 68 |\n",
+ "| AverageEpRet | -443 |\n",
+ "| StdEpRet | 266 |\n",
+ "| MaxEpRet | -17.7 |\n",
+ "| MinEpRet | -926 |\n",
+ "| AverageTestEpRet | -498 |\n",
+ "| StdTestEpRet | 213 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -779 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.5e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 71.7 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 76.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.3 |\n",
+ "| LossQ | 311 |\n",
+ "| Time | 1.89e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 69 |\n",
+ "| AverageEpRet | -542 |\n",
+ "| StdEpRet | 422 |\n",
+ "| MaxEpRet | -74.6 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 205 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -905 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.52e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 57.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 106 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.1 |\n",
+ "| LossQ | 315 |\n",
+ "| Time | 1.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 70 |\n",
+ "| AverageEpRet | -556 |\n",
+ "| StdEpRet | 188 |\n",
+ "| MaxEpRet | -166 |\n",
+ "| MinEpRet | -827 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -247 |\n",
+ "| MinTestEpRet | -782 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 104 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 89.6 |\n",
+ "| LossQ | 298 |\n",
+ "| Time | 1.96e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 71 |\n",
+ "| AverageEpRet | -508 |\n",
+ "| StdEpRet | 360 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.65e+03 |\n",
+ "| AverageTestEpRet | -503 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -98.4 |\n",
+ "| MinTestEpRet | -933 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.56e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 68.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 62.4 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.9 |\n",
+ "| LossQ | 310 |\n",
+ "| Time | 1.98e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 72 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -154 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 299 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.58e+05 |\n",
+ "| AverageQ1Vals | -98.4 |\n",
+ "| StdQ1Vals | 157 |\n",
+ "| MaxQ1Vals | 40.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -98.4 |\n",
+ "| StdQ2Vals | 157 |\n",
+ "| MaxQ2Vals | 65.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 307 |\n",
+ "| Time | 2.01e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 73 |\n",
+ "| AverageEpRet | -531 |\n",
+ "| StdEpRet | 253 |\n",
+ "| MaxEpRet | -152 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -403 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -66.2 |\n",
+ "| MinTestEpRet | -865 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.61e+05 |\n",
+ "| AverageQ1Vals | -98 |\n",
+ "| StdQ1Vals | 156 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -98 |\n",
+ "| StdQ2Vals | 156 |\n",
+ "| MaxQ2Vals | 61.2 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 290 |\n",
+ "| Time | 2.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 74 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 225 |\n",
+ "| MaxEpRet | -242 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -421 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -972 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.63e+05 |\n",
+ "| AverageQ1Vals | -96.8 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 84.5 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -96.8 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 44 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 87.6 |\n",
+ "| LossQ | 280 |\n",
+ "| Time | 2.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 75 |\n",
+ "| AverageEpRet | -438 |\n",
+ "| StdEpRet | 207 |\n",
+ "| MaxEpRet | -103 |\n",
+ "| MinEpRet | -930 |\n",
+ "| AverageTestEpRet | -416 |\n",
+ "| StdTestEpRet | 241 |\n",
+ "| MaxTestEpRet | -135 |\n",
+ "| MinTestEpRet | -887 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.65e+05 |\n",
+ "| AverageQ1Vals | -96.7 |\n",
+ "| StdQ1Vals | 155 |\n",
+ "| MaxQ1Vals | 72.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -96.7 |\n",
+ "| StdQ2Vals | 155 |\n",
+ "| MaxQ2Vals | 75.7 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 88.1 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 76 |\n",
+ "| AverageEpRet | -639 |\n",
+ "| StdEpRet | 315 |\n",
+ "| MaxEpRet | -214 |\n",
+ "| MinEpRet | -1.51e+03 |\n",
+ "| AverageTestEpRet | -437 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -19.2 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.67e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 48.5 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 46.2 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 86.8 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.14e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 77 |\n",
+ "| AverageEpRet | -468 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -901 |\n",
+ "| AverageTestEpRet | -556 |\n",
+ "| StdTestEpRet | 262 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -812 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.69e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 37.2 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 49.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.9 |\n",
+ "| LossQ | 283 |\n",
+ "| Time | 2.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 78 |\n",
+ "| AverageEpRet | -568 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -123 |\n",
+ "| MinEpRet | -1.27e+03 |\n",
+ "| AverageTestEpRet | -550 |\n",
+ "| StdTestEpRet | 360 |\n",
+ "| MaxTestEpRet | -164 |\n",
+ "| MinTestEpRet | -1.26e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.72e+05 |\n",
+ "| AverageQ1Vals | -94.5 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 47.8 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -94.5 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 92 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 85.9 |\n",
+ "| LossQ | 265 |\n",
+ "| Time | 2.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 79 |\n",
+ "| AverageEpRet | -566 |\n",
+ "| StdEpRet | 337 |\n",
+ "| MaxEpRet | -96.5 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -543 |\n",
+ "| StdTestEpRet | 365 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.74e+05 |\n",
+ "| AverageQ1Vals | -94.8 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 69.7 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -94.8 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 75.6 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 278 |\n",
+ "| Time | 2.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 80 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 290 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 196 |\n",
+ "| MaxTestEpRet | -182 |\n",
+ "| MinTestEpRet | -783 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+05 |\n",
+ "| AverageQ1Vals | -94.6 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 59.6 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -94.6 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 74.6 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 271 |\n",
+ "| Time | 2.28e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 81 |\n",
+ "| AverageEpRet | -535 |\n",
+ "| StdEpRet | 217 |\n",
+ "| MaxEpRet | -131 |\n",
+ "| MinEpRet | -999 |\n",
+ "| AverageTestEpRet | -422 |\n",
+ "| StdTestEpRet | 270 |\n",
+ "| MaxTestEpRet | -67.3 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.78e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 150 |\n",
+ "| MaxQ1Vals | 47.4 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 150 |\n",
+ "| MaxQ2Vals | 71 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 85.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.31e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 82 |\n",
+ "| AverageEpRet | -505 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -79.9 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -734 |\n",
+ "| StdTestEpRet | 289 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.22e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.8e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 149 |\n",
+ "| MaxQ1Vals | 53 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 149 |\n",
+ "| MaxQ2Vals | 67.3 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 86.6 |\n",
+ "| LossQ | 267 |\n",
+ "| Time | 2.35e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 83 |\n",
+ "| AverageEpRet | -585 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -96.7 |\n",
+ "| MinEpRet | -1.3e+03 |\n",
+ "| AverageTestEpRet | -436 |\n",
+ "| StdTestEpRet | 169 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -748 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.83e+05 |\n",
+ "| AverageQ1Vals | -92.9 |\n",
+ "| StdQ1Vals | 147 |\n",
+ "| MaxQ1Vals | 44.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -92.9 |\n",
+ "| StdQ2Vals | 147 |\n",
+ "| MaxQ2Vals | 75.2 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 84.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.38e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 84 |\n",
+ "| AverageEpRet | -406 |\n",
+ "| StdEpRet | 232 |\n",
+ "| MaxEpRet | -128 |\n",
+ "| MinEpRet | -924 |\n",
+ "| AverageTestEpRet | -650 |\n",
+ "| StdTestEpRet | 236 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.85e+05 |\n",
+ "| AverageQ1Vals | -92.5 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 46.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -92.5 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 80.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 84.4 |\n",
+ "| LossQ | 258 |\n",
+ "| Time | 2.41e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 85 |\n",
+ "| AverageEpRet | -474 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -180 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -527 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -166 |\n",
+ "| MinTestEpRet | -737 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.87e+05 |\n",
+ "| AverageQ1Vals | -91.8 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 51.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -91.8 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 33.2 |\n",
+ "| MinQ2Vals | -1.06e+03 |\n",
+ "| LossPi | 84.1 |\n",
+ "| LossQ | 249 |\n",
+ "| Time | 2.44e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 86 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 264 |\n",
+ "| MaxEpRet | -112 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -665 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.89e+05 |\n",
+ "| AverageQ1Vals | -91.6 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 65.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -91.7 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 67.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 83.5 |\n",
+ "| LossQ | 238 |\n",
+ "| Time | 2.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 87 |\n",
+ "| AverageEpRet | -515 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -122 |\n",
+ "| MinEpRet | -976 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 202 |\n",
+ "| MaxTestEpRet | -180 |\n",
+ "| MinTestEpRet | -829 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.91e+05 |\n",
+ "| AverageQ1Vals | -92.1 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -92.1 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 54.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 83.6 |\n",
+ "| LossQ | 256 |\n",
+ "| Time | 2.51e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 88 |\n",
+ "| AverageEpRet | -572 |\n",
+ "| StdEpRet | 258 |\n",
+ "| MaxEpRet | -57 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -508 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -217 |\n",
+ "| MinTestEpRet | -794 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.94e+05 |\n",
+ "| AverageQ1Vals | -91.3 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 42.6 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -91.3 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 58 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 83.7 |\n",
+ "| LossQ | 246 |\n",
+ "| Time | 2.54e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 89 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 241 |\n",
+ "| MaxEpRet | -49.9 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -562 |\n",
+ "| StdTestEpRet | 306 |\n",
+ "| MaxTestEpRet | -68.2 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.96e+05 |\n",
+ "| AverageQ1Vals | -90.3 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -90.3 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 54.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 83 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.57e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 90 |\n",
+ "| AverageEpRet | -478 |\n",
+ "| StdEpRet | 286 |\n",
+ "| MaxEpRet | -68 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -603 |\n",
+ "| StdTestEpRet | 390 |\n",
+ "| MaxTestEpRet | -123 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.6 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.6e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 91 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 252 |\n",
+ "| MaxEpRet | -101 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -566 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -62.9 |\n",
+ "| MinTestEpRet | -1.02e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 25.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.7 |\n",
+ "| LossQ | 241 |\n",
+ "| Time | 2.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 92 |\n",
+ "| AverageEpRet | -425 |\n",
+ "| StdEpRet | 205 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -998 |\n",
+ "| AverageTestEpRet | -536 |\n",
+ "| StdTestEpRet | 271 |\n",
+ "| MaxTestEpRet | -209 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.02e+05 |\n",
+ "| AverageQ1Vals | -88.7 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -88.7 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 32.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 81.3 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.67e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 93 |\n",
+ "| AverageEpRet | -498 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -83.8 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -523 |\n",
+ "| StdTestEpRet | 286 |\n",
+ "| MaxTestEpRet | -83.5 |\n",
+ "| MinTestEpRet | -967 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.05e+05 |\n",
+ "| AverageQ1Vals | -88.2 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 97.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -88.2 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 36.8 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 80.8 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 94 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 231 |\n",
+ "| MaxEpRet | -88.9 |\n",
+ "| MinEpRet | -946 |\n",
+ "| AverageTestEpRet | -407 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -793 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.07e+05 |\n",
+ "| AverageQ1Vals | -87.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -87.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.6 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 80.4 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.73e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 95 |\n",
+ "| AverageEpRet | -471 |\n",
+ "| StdEpRet | 257 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -994 |\n",
+ "| AverageTestEpRet | -563 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -827 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.09e+05 |\n",
+ "| AverageQ1Vals | -87.2 |\n",
+ "| StdQ1Vals | 141 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -87.2 |\n",
+ "| StdQ2Vals | 141 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 80.3 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 96 |\n",
+ "| AverageEpRet | -473 |\n",
+ "| StdEpRet | 268 |\n",
+ "| MaxEpRet | -76.1 |\n",
+ "| MinEpRet | -1.03e+03 |\n",
+ "| AverageTestEpRet | -392 |\n",
+ "| StdTestEpRet | 191 |\n",
+ "| MaxTestEpRet | -90.2 |\n",
+ "| MinTestEpRet | -713 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.11e+05 |\n",
+ "| AverageQ1Vals | -86.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 32.9 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -86.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 40.5 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 79.5 |\n",
+ "| LossQ | 233 |\n",
+ "| Time | 2.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 97 |\n",
+ "| AverageEpRet | -497 |\n",
+ "| StdEpRet | 345 |\n",
+ "| MaxEpRet | -37.1 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 227 |\n",
+ "| MaxTestEpRet | -72.6 |\n",
+ "| MinTestEpRet | -742 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.13e+05 |\n",
+ "| AverageQ1Vals | -86.5 |\n",
+ "| StdQ1Vals | 140 |\n",
+ "| MaxQ1Vals | 43 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -86.5 |\n",
+ "| StdQ2Vals | 140 |\n",
+ "| MaxQ2Vals | 38.9 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 79.6 |\n",
+ "| LossQ | 234 |\n",
+ "| Time | 2.82e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 98 |\n",
+ "| AverageEpRet | -429 |\n",
+ "| StdEpRet | 215 |\n",
+ "| MaxEpRet | -135 |\n",
+ "| MinEpRet | -848 |\n",
+ "| AverageTestEpRet | -511 |\n",
+ "| StdTestEpRet | 337 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -1.37e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.16e+05 |\n",
+ "| AverageQ1Vals | -86.1 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -86.1 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43.7 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 79.1 |\n",
+ "| LossQ | 226 |\n",
+ "| Time | 2.85e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 99 |\n",
+ "| AverageEpRet | -514 |\n",
+ "| StdEpRet | 278 |\n",
+ "| MaxEpRet | -141 |\n",
+ "| MinEpRet | -1.21e+03 |\n",
+ "| AverageTestEpRet | -633 |\n",
+ "| StdTestEpRet | 489 |\n",
+ "| MaxTestEpRet | -181 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.18e+05 |\n",
+ "| AverageQ1Vals | -84.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -84.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 27.5 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 77.7 |\n",
+ "| LossQ | 225 |\n",
+ "| Time | 2.88e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 100 |\n",
+ "| AverageEpRet | -557 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -26 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -544 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -1.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+05 |\n",
+ "| AverageQ1Vals | -84 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 51.4 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -84 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43 |\n",
+ "| MinQ2Vals | -1.22e+03 |\n",
+ "| LossPi | 76.6 |\n",
+ "| LossQ | 220 |\n",
+ "| Time | 2.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 101 |\n",
+ "| AverageEpRet | -434 |\n",
+ "| StdEpRet | 192 |\n",
+ "| MaxEpRet | -155 |\n",
+ "| MinEpRet | -806 |\n",
+ "| AverageTestEpRet | -418 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -244 |\n",
+ "| MinTestEpRet | -730 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.22e+05 |\n",
+ "| AverageQ1Vals | -82.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 35.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -82.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 54.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 76.3 |\n",
+ "| LossQ | 219 |\n",
+ "| Time | 2.95e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 102 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -120 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -490 |\n",
+ "| StdTestEpRet | 223 |\n",
+ "| MaxTestEpRet | -73.4 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.24e+05 |\n",
+ "| AverageQ1Vals | -81.2 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 96.8 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -81.2 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 71.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 74.2 |\n",
+ "| LossQ | 213 |\n",
+ "| Time | 2.99e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 103 |\n",
+ "| AverageEpRet | -617 |\n",
+ "| StdEpRet | 327 |\n",
+ "| MaxEpRet | -104 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 214 |\n",
+ "| MaxTestEpRet | -248 |\n",
+ "| MinTestEpRet | -906 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.27e+05 |\n",
+ "| AverageQ1Vals | -80.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 70.5 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -80.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 41 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 74.1 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 104 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -65.7 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -425 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -774 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.29e+05 |\n",
+ "| AverageQ1Vals | -79.9 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 41.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -79.9 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 44.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 73.2 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 105 |\n",
+ "| AverageEpRet | -469 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -144 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -317 |\n",
+ "| StdTestEpRet | 137 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -541 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.31e+05 |\n",
+ "| AverageQ1Vals | -78.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 24.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -78.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 38.7 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.6 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 106 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 244 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -989 |\n",
+ "| AverageTestEpRet | -542 |\n",
+ "| StdTestEpRet | 277 |\n",
+ "| MaxTestEpRet | -129 |\n",
+ "| MinTestEpRet | -965 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.33e+05 |\n",
+ "| AverageQ1Vals | -79.1 |\n",
+ "| StdQ1Vals | 136 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -79.1 |\n",
+ "| StdQ2Vals | 136 |\n",
+ "| MaxQ2Vals | 54 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 72.7 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 107 |\n",
+ "| AverageEpRet | -571 |\n",
+ "| StdEpRet | 245 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -538 |\n",
+ "| StdTestEpRet | 189 |\n",
+ "| MaxTestEpRet | -255 |\n",
+ "| MinTestEpRet | -880 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.35e+05 |\n",
+ "| AverageQ1Vals | -78.4 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 33.8 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -78.4 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 71.6 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.15e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 108 |\n",
+ "| AverageEpRet | -481 |\n",
+ "| StdEpRet | 187 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -837 |\n",
+ "| AverageTestEpRet | -399 |\n",
+ "| StdTestEpRet | 162 |\n",
+ "| MaxTestEpRet | -149 |\n",
+ "| MinTestEpRet | -668 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.38e+05 |\n",
+ "| AverageQ1Vals | -78 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 34 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -78 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.1 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.18e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 109 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 272 |\n",
+ "| MaxEpRet | -63.6 |\n",
+ "| MinEpRet | -1.04e+03 |\n",
+ "| AverageTestEpRet | -531 |\n",
+ "| StdTestEpRet | 326 |\n",
+ "| MaxTestEpRet | -90.1 |\n",
+ "| MinTestEpRet | -1.19e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.4e+05 |\n",
+ "| AverageQ1Vals | -76.9 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 59 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.9 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 42.2 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.7 |\n",
+ "| LossQ | 212 |\n",
+ "| Time | 3.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 110 |\n",
+ "| AverageEpRet | -523 |\n",
+ "| StdEpRet | 321 |\n",
+ "| MaxEpRet | -29.2 |\n",
+ "| MinEpRet | -1.39e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 282 |\n",
+ "| MaxTestEpRet | -187 |\n",
+ "| MinTestEpRet | -1.03e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+05 |\n",
+ "| AverageQ1Vals | -76.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 43.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 53.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 70.9 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 111 |\n",
+ "| AverageEpRet | -453 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -81.5 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -524 |\n",
+ "| StdTestEpRet | 105 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -689 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.44e+05 |\n",
+ "| AverageQ1Vals | -76.5 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 30.1 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.5 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.8 |\n",
+ "| MinQ2Vals | -1.23e+03 |\n",
+ "| LossPi | 70.4 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 112 |\n",
+ "| AverageEpRet | -596 |\n",
+ "| StdEpRet | 262 |\n",
+ "| MaxEpRet | -173 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -415 |\n",
+ "| StdTestEpRet | 161 |\n",
+ "| MaxTestEpRet | -137 |\n",
+ "| MinTestEpRet | -652 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.46e+05 |\n",
+ "| AverageQ1Vals | -76.3 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 34.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.3 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 46.7 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.3 |\n",
+ "| LossQ | 198 |\n",
+ "| Time | 3.3e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 113 |\n",
+ "| AverageEpRet | -482 |\n",
+ "| StdEpRet | 333 |\n",
+ "| MaxEpRet | -62.8 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -559 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -142 |\n",
+ "| MinTestEpRet | -1.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.49e+05 |\n",
+ "| AverageQ1Vals | -76.2 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 38.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -76.2 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 50.3 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.33e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 114 |\n",
+ "| AverageEpRet | -450 |\n",
+ "| StdEpRet | 216 |\n",
+ "| MaxEpRet | -94.9 |\n",
+ "| MinEpRet | -872 |\n",
+ "| AverageTestEpRet | -432 |\n",
+ "| StdTestEpRet | 132 |\n",
+ "| MaxTestEpRet | -232 |\n",
+ "| MinTestEpRet | -645 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.51e+05 |\n",
+ "| AverageQ1Vals | -75.7 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 44.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -75.7 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 69.6 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.36e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 115 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 240 |\n",
+ "| MaxEpRet | -108 |\n",
+ "| MinEpRet | -932 |\n",
+ "| AverageTestEpRet | -371 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -143 |\n",
+ "| MinTestEpRet | -616 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.53e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 21.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 29.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 205 |\n",
+ "| Time | 3.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 116 |\n",
+ "| AverageEpRet | -538 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -960 |\n",
+ "| AverageTestEpRet | -475 |\n",
+ "| StdTestEpRet | 311 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -1.23e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.55e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 26 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 37.1 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 69.3 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.43e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 117 |\n",
+ "| AverageEpRet | -437 |\n",
+ "| StdEpRet | 203 |\n",
+ "| MaxEpRet | -92.8 |\n",
+ "| MinEpRet | -857 |\n",
+ "| AverageTestEpRet | -582 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -79.8 |\n",
+ "| MinTestEpRet | -861 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.57e+05 |\n",
+ "| AverageQ1Vals | -75.5 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 49.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -75.5 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 51 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 69.4 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.47e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 118 |\n",
+ "| AverageEpRet | -549 |\n",
+ "| StdEpRet | 236 |\n",
+ "| MaxEpRet | -111 |\n",
+ "| MinEpRet | -1.28e+03 |\n",
+ "| AverageTestEpRet | -588 |\n",
+ "| StdTestEpRet | 404 |\n",
+ "| MaxTestEpRet | -138 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.6e+05 |\n",
+ "| AverageQ1Vals | -75.1 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 31.7 |\n",
+ "| MinQ1Vals | -1.05e+03 |\n",
+ "| AverageQ2Vals | -75.1 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 38.6 |\n",
+ "| MinQ2Vals | -1.05e+03 |\n",
+ "| LossPi | 69 |\n",
+ "| LossQ | 192 |\n",
+ "| Time | 3.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 119 |\n",
+ "| AverageEpRet | -491 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -102 |\n",
+ "| MinEpRet | -945 |\n",
+ "| AverageTestEpRet | -535 |\n",
+ "| StdTestEpRet | 217 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -879 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.62e+05 |\n",
+ "| AverageQ1Vals | -74.9 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 60.9 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.9 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 28.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 69.2 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 120 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 249 |\n",
+ "| MaxEpRet | -64.6 |\n",
+ "| MinEpRet | -885 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 242 |\n",
+ "| MaxTestEpRet | -56.1 |\n",
+ "| MinTestEpRet | -838 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+05 |\n",
+ "| AverageQ1Vals | -74.8 |\n",
+ "| StdQ1Vals | 129 |\n",
+ "| MaxQ1Vals | 105 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.8 |\n",
+ "| StdQ2Vals | 129 |\n",
+ "| MaxQ2Vals | 77.4 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 69.1 |\n",
+ "| LossQ | 197 |\n",
+ "| Time | 3.56e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Setup experiment\n",
+ "logger_kwargs = dict(output_dir='model', exp_name='v0')\n",
+ "\n",
+ "# Run training\n",
+ "spinup.td3_pytorch(GyroscopeEnv, seed=0, steps_per_epoch=110*20, epochs=120, replay_size=1000000, gamma=0.95,\n",
+ "polyak=0.995, batch_size=100, start_steps=20000, update_after=800, update_every=50,max_ep_len=110,logger_kwargs=logger_kwargs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Gn3Gp40bcOVz"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 972,
+ "status": "ok",
+ "timestamp": 1584036455886,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "6GyY0wE-QBOj",
+ "outputId": "9a5e9011-e024-4a65-8383-83c062cdcad9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-900.18321242575\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "env = GyroscopeEnv()\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = torch.load('model/pyt_save/model.pt')\n",
+ "\n",
+ "# Test parameters\n",
+ "x1,x2,x3,x4,x1_ref,x3_ref,w = 0,1,0,1,1,3,25\n",
+ "state = env.reset(np.array([x1,x2,x3,x4,x1_ref,x3_ref,w]))\n",
+ "val = []\n",
+ "act = []\n",
+ "dt = 0.01\n",
+ "time = np.arange(0, 4, dt)\n",
+ "score = 0\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " action = agent.act(torch.as_tensor(state, dtype=torch.float32))\n",
+ " act.append(action)\n",
+ " state, reward, done, _ = env.step(action)\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ "\n",
+ "env.close()\n",
+ "print(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "vvMjuRHDcfrE"
+ },
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 1856,
+ "status": "ok",
+ "timestamp": 1584036457424,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "aCZCqujgcMVA",
+ "outputId": "05490294-aab8-4933-ca9b-e13ba85ecf6d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2160x2160 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(4,2,figsize=(30,30))\n",
+ "plt.subplot(4,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "plt.plot(time, [row[4] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,2)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "plt.plot(time, [row[5] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,3)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[0]- row[4]) for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[2]- row[5]) for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,7)\n",
+ "plt.title('Red gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u1 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in act],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,8)\n",
+ "plt.title('Blue gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u2 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in act],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fz9r-gaStzpk"
+ },
+ "source": [
+ "## 3D rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "HQM1E8JYc-cC"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 400\n",
+ "\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "gyroscope_ddpg_testing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/training_spinuplib/gyroscope_paramsearch_spinuplib.ipynb b/code/training_spinuplib/gyroscope_paramsearch_spinuplib.ipynb
new file mode 100644
index 0000000..79898bd
--- /dev/null
+++ b/code/training_spinuplib/gyroscope_paramsearch_spinuplib.ipynb
@@ -0,0 +1,3786 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "x83dMPapQBN6"
+ },
+ "source": [
+ "# Gyroscope TD3 hyperparameter search (spinup library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8inmkEG5QHqx"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 655,
+ "status": "ok",
+ "timestamp": 1584030129316,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "DnnJGcnsQvZC",
+ "outputId": "9d6d1408-da29-44ed-af74-4ebdc2bd865a"
+ },
+ "outputs": [],
+ "source": [
+ "cd drive/My\\ Drive/Colab Notebooks/DDPG_uda_v0/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "fuJhdd479TpP"
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name '_C' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-4-0ec252ad867d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msolve_ivp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeque\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m __all__ += [name for name in dir(_C)\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'_'\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m not name.endswith('Base')]\n",
+ "\u001b[0;31mNameError\u001b[0m: name '_C' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from collections import deque\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "from vpython import *\n",
+ "import argparse\n",
+ "\n",
+ "import spinup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "O0O0t5ZR9Tp6"
+ },
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Al_k1rtvQBOM"
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self):\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = 0.05\n",
+ " \n",
+ " # Error\n",
+ " self.int_diff_x1 = 0\n",
+ " self.int_diff_x3 = 0\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 100 * 2 * np.pi / 60\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiskSpeed = 300 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxAngle,self.maxAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, x1_ref, x3_ref, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " # Angle error\n",
+ " diff_x1 = angle_normalize(x1 - x1_ref)\n",
+ " diff_x3 = angle_normalize(x3 - x3_ref)\n",
+ " \n",
+ " # Integral of error\n",
+ " self.int_diff_x1 = self.int_diff_x1 + diff_x1\n",
+ " self.int_diff_x3 = self.int_diff_x3 + diff_x3\n",
+ " \n",
+ " # Reward 1: differentiable reward (LQR obj function)\n",
+ " reward = -((3*diff_x1)**2 + (3*diff_x3)**2 + (.2*x2)**2 + (.2*x4)**2 + (.1*u1)**2 + (.1*u2)**2)\\\n",
+ " #-(0.01*abs(self.int_diff_x1) + 0.01*abs(self.int_diff_x3))\n",
+ "\n",
+ " \"\"\"# Count time spent in goal:\n",
+ " if abs(diff_x1)<0.05 and abs(diff_x3)<0.05:\n",
+ " self.countGoal +=1\n",
+ " else:\n",
+ " self.countGoal = 0\n",
+ " \n",
+ " # Reward 2: sparse reward for staying in goal range for a long time \n",
+ " if self.countGoal >= (self.timeGoal)/self.dt: #max expected reward over length becomes 0 + (totaltime-goaltime)\n",
+ " reward += 1\"\"\"\n",
+ "\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,x1_ref, x3_ref,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, state = None):\n",
+ " \n",
+ " \n",
+ " # Generate random state (for training) or use given state (for simulation)\n",
+ " if state is None:\n",
+ " self.state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " self.state = state\n",
+ "\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = x[-1]\n",
+ "\n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "be0wYIeBQBOc"
+ },
+ "source": [
+ "## Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 533
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 654004,
+ "status": "error",
+ "timestamp": 1584037207187,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "fLyFHs0yQBOd",
+ "outputId": "260489ff-5e40-416a-e529-5a0cfcaefceb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Log dir model already exists! Storing info there anyway.\n",
+ "\u001b[32;1mLogging data to model/progress.txt\u001b[0m\n",
+ "\u001b[36;1mSaving config:\n",
+ "\u001b[0m\n",
+ "{\n",
+ " \"ac_kwargs\":\t{},\n",
+ " \"act_noise\":\t0.1,\n",
+ " \"actor_critic\":\t\"MLPActorCritic\",\n",
+ " \"batch_size\":\t100,\n",
+ " \"env_fn\":\t\"GyroscopeEnv\",\n",
+ " \"epochs\":\t120,\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"gamma\":\t0.95,\n",
+ " \"logger\":\t{\n",
+ " \"<spinup.utils.logx.EpochLogger object at 0x7f7e800ca588>\":\t{\n",
+ " \"epoch_dict\":\t{},\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"first_row\":\ttrue,\n",
+ " \"log_current_row\":\t{},\n",
+ " \"log_headers\":\t[],\n",
+ " \"output_dir\":\t\"model\",\n",
+ " \"output_file\":\t{\n",
+ " \"<_io.TextIOWrapper name='model/progress.txt' mode='w' encoding='UTF-8'>\":\t{\n",
+ " \"mode\":\t\"w\"\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"logger_kwargs\":\t{\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"output_dir\":\t\"model\"\n",
+ " },\n",
+ " \"max_ep_len\":\t110,\n",
+ " \"noise_clip\":\t0.5,\n",
+ " \"num_test_episodes\":\t10,\n",
+ " \"pi_lr\":\t0.001,\n",
+ " \"policy_delay\":\t2,\n",
+ " \"polyak\":\t0.995,\n",
+ " \"q_lr\":\t0.001,\n",
+ " \"replay_size\":\t1000000,\n",
+ " \"save_freq\":\t1,\n",
+ " \"seed\":\t0,\n",
+ " \"start_steps\":\t20000,\n",
+ " \"steps_per_epoch\":\t2200,\n",
+ " \"target_noise\":\t0.2,\n",
+ " \"update_after\":\t800,\n",
+ " \"update_every\":\t50\n",
+ "}\n",
+ "\u001b[32;1m\n",
+ "Number of parameters: \t pi: 68354, \t q1: 68609, \t q2: 68609\n",
+ "\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 1 |\n",
+ "| AverageEpRet | -6.44e+03 |\n",
+ "| StdEpRet | 1.49e+03 |\n",
+ "| MaxEpRet | -4.55e+03 |\n",
+ "| MinEpRet | -9.52e+03 |\n",
+ "| AverageTestEpRet | -7.13e+03 |\n",
+ "| StdTestEpRet | 1.25e+03 |\n",
+ "| MaxTestEpRet | -4.01e+03 |\n",
+ "| MinTestEpRet | -8.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+03 |\n",
+ "| AverageQ1Vals | -123 |\n",
+ "| StdQ1Vals | 54.5 |\n",
+ "| MaxQ1Vals | 0.0809 |\n",
+ "| MinQ1Vals | -364 |\n",
+ "| AverageQ2Vals | -123 |\n",
+ "| StdQ2Vals | 54.6 |\n",
+ "| MaxQ2Vals | 1.77 |\n",
+ "| MinQ2Vals | -373 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 1.95e+03 |\n",
+ "| Time | 15.8 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 2 |\n",
+ "| AverageEpRet | -6.72e+03 |\n",
+ "| StdEpRet | 1.81e+03 |\n",
+ "| MaxEpRet | -4.58e+03 |\n",
+ "| MinEpRet | -1.07e+04 |\n",
+ "| AverageTestEpRet | -8.21e+03 |\n",
+ "| StdTestEpRet | 1.1e+03 |\n",
+ "| MaxTestEpRet | -6.46e+03 |\n",
+ "| MinTestEpRet | -1.01e+04 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+03 |\n",
+ "| AverageQ1Vals | -276 |\n",
+ "| StdQ1Vals | 98.9 |\n",
+ "| MaxQ1Vals | -54.4 |\n",
+ "| MinQ1Vals | -672 |\n",
+ "| AverageQ2Vals | -276 |\n",
+ "| StdQ2Vals | 99.2 |\n",
+ "| MaxQ2Vals | -51.6 |\n",
+ "| MinQ2Vals | -686 |\n",
+ "| LossPi | 256 |\n",
+ "| LossQ | 2.12e+03 |\n",
+ "| Time | 42.1 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 3 |\n",
+ "| AverageEpRet | -7.08e+03 |\n",
+ "| StdEpRet | 1.22e+03 |\n",
+ "| MaxEpRet | -4.94e+03 |\n",
+ "| MinEpRet | -9.27e+03 |\n",
+ "| AverageTestEpRet | -5.91e+03 |\n",
+ "| StdTestEpRet | 1.83e+03 |\n",
+ "| MaxTestEpRet | -3.79e+03 |\n",
+ "| MinTestEpRet | -9.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+03 |\n",
+ "| AverageQ1Vals | -414 |\n",
+ "| StdQ1Vals | 128 |\n",
+ "| MaxQ1Vals | -36.3 |\n",
+ "| MinQ1Vals | -952 |\n",
+ "| AverageQ2Vals | -414 |\n",
+ "| StdQ2Vals | 128 |\n",
+ "| MaxQ2Vals | -26.8 |\n",
+ "| MinQ2Vals | -950 |\n",
+ "| LossPi | 382 |\n",
+ "| LossQ | 2.76e+03 |\n",
+ "| Time | 67.9 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 4 |\n",
+ "| AverageEpRet | -7.06e+03 |\n",
+ "| StdEpRet | 1.53e+03 |\n",
+ "| MaxEpRet | -4.98e+03 |\n",
+ "| MinEpRet | -1.09e+04 |\n",
+ "| AverageTestEpRet | -5.01e+03 |\n",
+ "| StdTestEpRet | 1.18e+03 |\n",
+ "| MaxTestEpRet | -3.34e+03 |\n",
+ "| MinTestEpRet | -6.44e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+03 |\n",
+ "| AverageQ1Vals | -501 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 11.2 |\n",
+ "| MinQ1Vals | -1.07e+03 |\n",
+ "| AverageQ2Vals | -501 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 16.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 459 |\n",
+ "| LossQ | 3.94e+03 |\n",
+ "| Time | 92 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 5 |\n",
+ "| AverageEpRet | -6.3e+03 |\n",
+ "| StdEpRet | 1.14e+03 |\n",
+ "| MaxEpRet | -3.87e+03 |\n",
+ "| MinEpRet | -8.1e+03 |\n",
+ "| AverageTestEpRet | -4.27e+03 |\n",
+ "| StdTestEpRet | 1.52e+03 |\n",
+ "| MaxTestEpRet | -1.82e+03 |\n",
+ "| MinTestEpRet | -6.62e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+04 |\n",
+ "| AverageQ1Vals | -552 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | -15.7 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -552 |\n",
+ "| StdQ2Vals | 172 |\n",
+ "| MaxQ2Vals | 17.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 508 |\n",
+ "| LossQ | 4.63e+03 |\n",
+ "| Time | 116 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 6 |\n",
+ "| AverageEpRet | -6.76e+03 |\n",
+ "| StdEpRet | 1.35e+03 |\n",
+ "| MaxEpRet | -4.61e+03 |\n",
+ "| MinEpRet | -1.13e+04 |\n",
+ "| AverageTestEpRet | -3.67e+03 |\n",
+ "| StdTestEpRet | 994 |\n",
+ "| MaxTestEpRet | -2.24e+03 |\n",
+ "| MinTestEpRet | -5.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+04 |\n",
+ "| AverageQ1Vals | -574 |\n",
+ "| StdQ1Vals | 186 |\n",
+ "| MaxQ1Vals | -39.4 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -574 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | -27.9 |\n",
+ "| MinQ2Vals | -1.29e+03 |\n",
+ "| LossPi | 527 |\n",
+ "| LossQ | 5.18e+03 |\n",
+ "| Time | 139 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 7 |\n",
+ "| AverageEpRet | -7.02e+03 |\n",
+ "| StdEpRet | 1.79e+03 |\n",
+ "| MaxEpRet | -3.34e+03 |\n",
+ "| MinEpRet | -9.5e+03 |\n",
+ "| AverageTestEpRet | -3.05e+03 |\n",
+ "| StdTestEpRet | 1.56e+03 |\n",
+ "| MaxTestEpRet | -1.34e+03 |\n",
+ "| MinTestEpRet | -6.7e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+04 |\n",
+ "| AverageQ1Vals | -582 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | -68.9 |\n",
+ "| MinQ1Vals | -1.38e+03 |\n",
+ "| AverageQ2Vals | -582 |\n",
+ "| StdQ2Vals | 195 |\n",
+ "| MaxQ2Vals | -43.9 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 534 |\n",
+ "| LossQ | 5.26e+03 |\n",
+ "| Time | 163 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 8 |\n",
+ "| AverageEpRet | -6.62e+03 |\n",
+ "| StdEpRet | 1.42e+03 |\n",
+ "| MaxEpRet | -4.04e+03 |\n",
+ "| MinEpRet | -9.02e+03 |\n",
+ "| AverageTestEpRet | -1.54e+03 |\n",
+ "| StdTestEpRet | 771 |\n",
+ "| MaxTestEpRet | -595 |\n",
+ "| MinTestEpRet | -3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+04 |\n",
+ "| AverageQ1Vals | -573 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | -4.68 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -573 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 22.9 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 522 |\n",
+ "| LossQ | 5.51e+03 |\n",
+ "| Time | 187 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 9 |\n",
+ "| AverageEpRet | -6.7e+03 |\n",
+ "| StdEpRet | 1.58e+03 |\n",
+ "| MaxEpRet | -4.21e+03 |\n",
+ "| MinEpRet | -9.46e+03 |\n",
+ "| AverageTestEpRet | -842 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -411 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+04 |\n",
+ "| AverageQ1Vals | -546 |\n",
+ "| StdQ1Vals | 223 |\n",
+ "| MaxQ1Vals | 71.2 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -546 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 60.5 |\n",
+ "| MinQ2Vals | -1.41e+03 |\n",
+ "| LossPi | 490 |\n",
+ "| LossQ | 5.83e+03 |\n",
+ "| Time | 211 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 10 |\n",
+ "| AverageEpRet | -1.66e+03 |\n",
+ "| StdEpRet | 1.99e+03 |\n",
+ "| MaxEpRet | -153 |\n",
+ "| MinEpRet | -8.64e+03 |\n",
+ "| AverageTestEpRet | -872 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -487 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+04 |\n",
+ "| AverageQ1Vals | -488 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 151 |\n",
+ "| MinQ1Vals | -1.43e+03 |\n",
+ "| AverageQ2Vals | -488 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 127 |\n",
+ "| MinQ2Vals | -1.4e+03 |\n",
+ "| LossPi | 432 |\n",
+ "| LossQ | 5.85e+03 |\n",
+ "| Time | 239 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 11 |\n",
+ "| AverageEpRet | -947 |\n",
+ "| StdEpRet | 493 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.89e+03 |\n",
+ "| AverageTestEpRet | -1.09e+03 |\n",
+ "| StdTestEpRet | 661 |\n",
+ "| MaxTestEpRet | -271 |\n",
+ "| MinTestEpRet | -2.78e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+04 |\n",
+ "| AverageQ1Vals | -425 |\n",
+ "| StdQ1Vals | 258 |\n",
+ "| MaxQ1Vals | 141 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -425 |\n",
+ "| StdQ2Vals | 258 |\n",
+ "| MaxQ2Vals | 137 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 376 |\n",
+ "| LossQ | 4.71e+03 |\n",
+ "| Time | 263 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 12 |\n",
+ "| AverageEpRet | -888 |\n",
+ "| StdEpRet | 472 |\n",
+ "| MaxEpRet | -325 |\n",
+ "| MinEpRet | -2.33e+03 |\n",
+ "| AverageTestEpRet | -602 |\n",
+ "| StdTestEpRet | 199 |\n",
+ "| MaxTestEpRet | -286 |\n",
+ "| MinTestEpRet | -910 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+04 |\n",
+ "| AverageQ1Vals | -384 |\n",
+ "| StdQ1Vals | 264 |\n",
+ "| MaxQ1Vals | 161 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -384 |\n",
+ "| StdQ2Vals | 264 |\n",
+ "| MaxQ2Vals | 142 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 341 |\n",
+ "| LossQ | 3.72e+03 |\n",
+ "| Time | 288 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 13 |\n",
+ "| AverageEpRet | -797 |\n",
+ "| StdEpRet | 536 |\n",
+ "| MaxEpRet | -48.6 |\n",
+ "| MinEpRet | -2.46e+03 |\n",
+ "| AverageTestEpRet | -857 |\n",
+ "| StdTestEpRet | 354 |\n",
+ "| MaxTestEpRet | -467 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.86e+04 |\n",
+ "| AverageQ1Vals | -354 |\n",
+ "| StdQ1Vals | 270 |\n",
+ "| MaxQ1Vals | 169 |\n",
+ "| MinQ1Vals | -1.36e+03 |\n",
+ "| AverageQ2Vals | -354 |\n",
+ "| StdQ2Vals | 270 |\n",
+ "| MaxQ2Vals | 166 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 314 |\n",
+ "| LossQ | 3.07e+03 |\n",
+ "| Time | 312 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 14 |\n",
+ "| AverageEpRet | -926 |\n",
+ "| StdEpRet | 418 |\n",
+ "| MaxEpRet | -333 |\n",
+ "| MinEpRet | -1.67e+03 |\n",
+ "| AverageTestEpRet | -902 |\n",
+ "| StdTestEpRet | 481 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -2.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.08e+04 |\n",
+ "| AverageQ1Vals | -326 |\n",
+ "| StdQ1Vals | 274 |\n",
+ "| MaxQ1Vals | 192 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -326 |\n",
+ "| StdQ2Vals | 274 |\n",
+ "| MaxQ2Vals | 191 |\n",
+ "| MinQ2Vals | -1.34e+03 |\n",
+ "| LossPi | 288 |\n",
+ "| LossQ | 2.66e+03 |\n",
+ "| Time | 337 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 15 |\n",
+ "| AverageEpRet | -738 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -267 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -1.45e+03 |\n",
+ "| StdTestEpRet | 1.42e+03 |\n",
+ "| MaxTestEpRet | -185 |\n",
+ "| MinTestEpRet | -4.34e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.3e+04 |\n",
+ "| AverageQ1Vals | -297 |\n",
+ "| StdQ1Vals | 279 |\n",
+ "| MaxQ1Vals | 220 |\n",
+ "| MinQ1Vals | -1.31e+03 |\n",
+ "| AverageQ2Vals | -297 |\n",
+ "| StdQ2Vals | 279 |\n",
+ "| MaxQ2Vals | 221 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 261 |\n",
+ "| LossQ | 2.37e+03 |\n",
+ "| Time | 362 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 16 |\n",
+ "| AverageEpRet | -1.37e+03 |\n",
+ "| StdEpRet | 1.85e+03 |\n",
+ "| MaxEpRet | -223 |\n",
+ "| MinEpRet | -8.63e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 159 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -913 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.52e+04 |\n",
+ "| AverageQ1Vals | -267 |\n",
+ "| StdQ1Vals | 277 |\n",
+ "| MaxQ1Vals | 279 |\n",
+ "| MinQ1Vals | -1.3e+03 |\n",
+ "| AverageQ2Vals | -267 |\n",
+ "| StdQ2Vals | 277 |\n",
+ "| MaxQ2Vals | 269 |\n",
+ "| MinQ2Vals | -1.27e+03 |\n",
+ "| LossPi | 231 |\n",
+ "| LossQ | 2.21e+03 |\n",
+ "| Time | 386 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 17 |\n",
+ "| AverageEpRet | -754 |\n",
+ "| StdEpRet | 595 |\n",
+ "| MaxEpRet | -202 |\n",
+ "| MinEpRet | -2.5e+03 |\n",
+ "| AverageTestEpRet | -636 |\n",
+ "| StdTestEpRet | 389 |\n",
+ "| MaxTestEpRet | -147 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.74e+04 |\n",
+ "| AverageQ1Vals | -245 |\n",
+ "| StdQ1Vals | 273 |\n",
+ "| MaxQ1Vals | 278 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -245 |\n",
+ "| StdQ2Vals | 273 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 211 |\n",
+ "| LossQ | 2.03e+03 |\n",
+ "| Time | 411 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 18 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -241 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -713 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -419 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.96e+04 |\n",
+ "| AverageQ1Vals | -226 |\n",
+ "| StdQ1Vals | 268 |\n",
+ "| MaxQ1Vals | 275 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -226 |\n",
+ "| StdQ2Vals | 268 |\n",
+ "| MaxQ2Vals | 265 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 194 |\n",
+ "| LossQ | 1.83e+03 |\n",
+ "| Time | 434 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 19 |\n",
+ "| AverageEpRet | -745 |\n",
+ "| StdEpRet | 537 |\n",
+ "| MaxEpRet | -109 |\n",
+ "| MinEpRet | -2.14e+03 |\n",
+ "| AverageTestEpRet | -963 |\n",
+ "| StdTestEpRet | 641 |\n",
+ "| MaxTestEpRet | -159 |\n",
+ "| MinTestEpRet | -2.65e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.18e+04 |\n",
+ "| AverageQ1Vals | -214 |\n",
+ "| StdQ1Vals | 263 |\n",
+ "| MaxQ1Vals | 273 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -214 |\n",
+ "| StdQ2Vals | 263 |\n",
+ "| MaxQ2Vals | 266 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 183 |\n",
+ "| LossQ | 1.67e+03 |\n",
+ "| Time | 458 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 20 |\n",
+ "| AverageEpRet | -711 |\n",
+ "| StdEpRet | 511 |\n",
+ "| MaxEpRet | -18.3 |\n",
+ "| MinEpRet | -1.83e+03 |\n",
+ "| AverageTestEpRet | -803 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -126 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+04 |\n",
+ "| AverageQ1Vals | -198 |\n",
+ "| StdQ1Vals | 260 |\n",
+ "| MaxQ1Vals | 276 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -198 |\n",
+ "| StdQ2Vals | 260 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 169 |\n",
+ "| LossQ | 1.57e+03 |\n",
+ "| Time | 483 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 21 |\n",
+ "| AverageEpRet | -664 |\n",
+ "| StdEpRet | 403 |\n",
+ "| MaxEpRet | -176 |\n",
+ "| MinEpRet | -1.87e+03 |\n",
+ "| AverageTestEpRet | -577 |\n",
+ "| StdTestEpRet | 343 |\n",
+ "| MaxTestEpRet | -133 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.62e+04 |\n",
+ "| AverageQ1Vals | -187 |\n",
+ "| StdQ1Vals | 254 |\n",
+ "| MaxQ1Vals | 269 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -187 |\n",
+ "| StdQ2Vals | 254 |\n",
+ "| MaxQ2Vals | 262 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 159 |\n",
+ "| LossQ | 1.48e+03 |\n",
+ "| Time | 507 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 22 |\n",
+ "| AverageEpRet | -662 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -261 |\n",
+ "| MinEpRet | -1.64e+03 |\n",
+ "| AverageTestEpRet | -780 |\n",
+ "| StdTestEpRet | 458 |\n",
+ "| MaxTestEpRet | -39.9 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.84e+04 |\n",
+ "| AverageQ1Vals | -178 |\n",
+ "| StdQ1Vals | 249 |\n",
+ "| MaxQ1Vals | 260 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -178 |\n",
+ "| StdQ2Vals | 249 |\n",
+ "| MaxQ2Vals | 258 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 151 |\n",
+ "| LossQ | 1.35e+03 |\n",
+ "| Time | 531 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 23 |\n",
+ "| AverageEpRet | -524 |\n",
+ "| StdEpRet | 312 |\n",
+ "| MaxEpRet | -85.9 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -768 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -329 |\n",
+ "| MinTestEpRet | -1.3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.06e+04 |\n",
+ "| AverageQ1Vals | -171 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 239 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -170 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 246 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 145 |\n",
+ "| LossQ | 1.29e+03 |\n",
+ "| Time | 555 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 24 |\n",
+ "| AverageEpRet | -714 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -181 |\n",
+ "| MinEpRet | -1.29e+03 |\n",
+ "| AverageTestEpRet | -433 |\n",
+ "| StdTestEpRet | 184 |\n",
+ "| MaxTestEpRet | -178 |\n",
+ "| MinTestEpRet | -786 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.28e+04 |\n",
+ "| AverageQ1Vals | -164 |\n",
+ "| StdQ1Vals | 238 |\n",
+ "| MaxQ1Vals | 227 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -164 |\n",
+ "| StdQ2Vals | 238 |\n",
+ "| MaxQ2Vals | 229 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 139 |\n",
+ "| LossQ | 1.22e+03 |\n",
+ "| Time | 579 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 25 |\n",
+ "| AverageEpRet | -709 |\n",
+ "| StdEpRet | 425 |\n",
+ "| MaxEpRet | -88.8 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -808 |\n",
+ "| StdTestEpRet | 476 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.81e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.5e+04 |\n",
+ "| AverageQ1Vals | -159 |\n",
+ "| StdQ1Vals | 233 |\n",
+ "| MaxQ1Vals | 209 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -159 |\n",
+ "| StdQ2Vals | 233 |\n",
+ "| MaxQ2Vals | 209 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.15e+03 |\n",
+ "| Time | 603 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 26 |\n",
+ "| AverageEpRet | -813 |\n",
+ "| StdEpRet | 433 |\n",
+ "| MaxEpRet | -247 |\n",
+ "| MinEpRet | -1.81e+03 |\n",
+ "| AverageTestEpRet | -702 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.72e+04 |\n",
+ "| AverageQ1Vals | -158 |\n",
+ "| StdQ1Vals | 231 |\n",
+ "| MaxQ1Vals | 194 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -158 |\n",
+ "| StdQ2Vals | 231 |\n",
+ "| MaxQ2Vals | 198 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.14e+03 |\n",
+ "| Time | 628 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 27 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 282 |\n",
+ "| MaxEpRet | -336 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -934 |\n",
+ "| StdTestEpRet | 626 |\n",
+ "| MaxTestEpRet | -317 |\n",
+ "| MinTestEpRet | -2.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.94e+04 |\n",
+ "| AverageQ1Vals | -157 |\n",
+ "| StdQ1Vals | 229 |\n",
+ "| MaxQ1Vals | 190 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -157 |\n",
+ "| StdQ2Vals | 229 |\n",
+ "| MaxQ2Vals | 192 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 135 |\n",
+ "| LossQ | 1.03e+03 |\n",
+ "| Time | 653 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 28 |\n",
+ "| AverageEpRet | -965 |\n",
+ "| StdEpRet | 522 |\n",
+ "| MaxEpRet | -172 |\n",
+ "| MinEpRet | -2.3e+03 |\n",
+ "| AverageTestEpRet | -599 |\n",
+ "| StdTestEpRet | 398 |\n",
+ "| MaxTestEpRet | -163 |\n",
+ "| MinTestEpRet | -1.32e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.16e+04 |\n",
+ "| AverageQ1Vals | -154 |\n",
+ "| StdQ1Vals | 226 |\n",
+ "| MaxQ1Vals | 176 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -154 |\n",
+ "| StdQ2Vals | 226 |\n",
+ "| MaxQ2Vals | 188 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 973 |\n",
+ "| Time | 677 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 29 |\n",
+ "| AverageEpRet | -766 |\n",
+ "| StdEpRet | 367 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.69e+03 |\n",
+ "| AverageTestEpRet | -659 |\n",
+ "| StdTestEpRet | 229 |\n",
+ "| MaxTestEpRet | -303 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.38e+04 |\n",
+ "| AverageQ1Vals | -153 |\n",
+ "| StdQ1Vals | 225 |\n",
+ "| MaxQ1Vals | 173 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -153 |\n",
+ "| StdQ2Vals | 225 |\n",
+ "| MaxQ2Vals | 176 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 961 |\n",
+ "| Time | 702 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 30 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -388 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -686 |\n",
+ "| StdTestEpRet | 188 |\n",
+ "| MaxTestEpRet | -312 |\n",
+ "| MinTestEpRet | -977 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+04 |\n",
+ "| AverageQ1Vals | -151 |\n",
+ "| StdQ1Vals | 222 |\n",
+ "| MaxQ1Vals | 162 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -151 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 159 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 131 |\n",
+ "| LossQ | 913 |\n",
+ "| Time | 727 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 31 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 442 |\n",
+ "| MaxEpRet | -163 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -678 |\n",
+ "| StdTestEpRet | 371 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.82e+04 |\n",
+ "| AverageQ1Vals | -149 |\n",
+ "| StdQ1Vals | 219 |\n",
+ "| MaxQ1Vals | 149 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -149 |\n",
+ "| StdQ2Vals | 219 |\n",
+ "| MaxQ2Vals | 153 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 855 |\n",
+ "| Time | 753 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 32 |\n",
+ "| AverageEpRet | -760 |\n",
+ "| StdEpRet | 313 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -1.49e+03 |\n",
+ "| AverageTestEpRet | -759 |\n",
+ "| StdTestEpRet | 430 |\n",
+ "| MaxTestEpRet | -256 |\n",
+ "| MinTestEpRet | -1.64e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.04e+04 |\n",
+ "| AverageQ1Vals | -150 |\n",
+ "| StdQ1Vals | 217 |\n",
+ "| MaxQ1Vals | 146 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -150 |\n",
+ "| StdQ2Vals | 217 |\n",
+ "| MaxQ2Vals | 145 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 835 |\n",
+ "| Time | 775 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 33 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 395 |\n",
+ "| MaxEpRet | -57.1 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 368 |\n",
+ "| MaxTestEpRet | -120 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.26e+04 |\n",
+ "| AverageQ1Vals | -147 |\n",
+ "| StdQ1Vals | 213 |\n",
+ "| MaxQ1Vals | 130 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -147 |\n",
+ "| StdQ2Vals | 213 |\n",
+ "| MaxQ2Vals | 125 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 129 |\n",
+ "| LossQ | 800 |\n",
+ "| Time | 796 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 34 |\n",
+ "| AverageEpRet | -732 |\n",
+ "| StdEpRet | 361 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.6e+03 |\n",
+ "| AverageTestEpRet | -737 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -278 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.48e+04 |\n",
+ "| AverageQ1Vals | -144 |\n",
+ "| StdQ1Vals | 211 |\n",
+ "| MaxQ1Vals | 133 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -144 |\n",
+ "| StdQ2Vals | 211 |\n",
+ "| MaxQ2Vals | 121 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 126 |\n",
+ "| LossQ | 784 |\n",
+ "| Time | 820 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 35 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -159 |\n",
+ "| MinEpRet | -1.58e+03 |\n",
+ "| AverageTestEpRet | -766 |\n",
+ "| StdTestEpRet | 358 |\n",
+ "| MaxTestEpRet | -85.9 |\n",
+ "| MinTestEpRet | -1.35e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.7e+04 |\n",
+ "| AverageQ1Vals | -143 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -143 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 107 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 730 |\n",
+ "| Time | 846 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 36 |\n",
+ "| AverageEpRet | -848 |\n",
+ "| StdEpRet | 733 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -3.32e+03 |\n",
+ "| AverageTestEpRet | -765 |\n",
+ "| StdTestEpRet | 340 |\n",
+ "| MaxTestEpRet | -191 |\n",
+ "| MinTestEpRet | -1.48e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.92e+04 |\n",
+ "| AverageQ1Vals | -141 |\n",
+ "| StdQ1Vals | 207 |\n",
+ "| MaxQ1Vals | 115 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -141 |\n",
+ "| StdQ2Vals | 207 |\n",
+ "| MaxQ2Vals | 108 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 703 |\n",
+ "| Time | 873 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 37 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 379 |\n",
+ "| MaxEpRet | -11.3 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -646 |\n",
+ "| StdTestEpRet | 399 |\n",
+ "| MaxTestEpRet | -102 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.14e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 204 |\n",
+ "| MaxQ1Vals | 103 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 204 |\n",
+ "| MaxQ2Vals | 96.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 667 |\n",
+ "| Time | 900 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 38 |\n",
+ "| AverageEpRet | -634 |\n",
+ "| StdEpRet | 304 |\n",
+ "| MaxEpRet | -44.1 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -716 |\n",
+ "| StdTestEpRet | 356 |\n",
+ "| MaxTestEpRet | -263 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.36e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 203 |\n",
+ "| MaxQ1Vals | 91.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 203 |\n",
+ "| MaxQ2Vals | 86 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 664 |\n",
+ "| Time | 929 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 39 |\n",
+ "| AverageEpRet | -630 |\n",
+ "| StdEpRet | 375 |\n",
+ "| MaxEpRet | -127 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -591 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -204 |\n",
+ "| MinTestEpRet | -946 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.58e+04 |\n",
+ "| AverageQ1Vals | -139 |\n",
+ "| StdQ1Vals | 201 |\n",
+ "| MaxQ1Vals | 86.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -139 |\n",
+ "| StdQ2Vals | 201 |\n",
+ "| MaxQ2Vals | 77.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 649 |\n",
+ "| Time | 960 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 40 |\n",
+ "| AverageEpRet | -506 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -67.2 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -621 |\n",
+ "| StdTestEpRet | 351 |\n",
+ "| MaxTestEpRet | -171 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+04 |\n",
+ "| AverageQ1Vals | -137 |\n",
+ "| StdQ1Vals | 199 |\n",
+ "| MaxQ1Vals | 77.5 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -137 |\n",
+ "| StdQ2Vals | 199 |\n",
+ "| MaxQ2Vals | 66.2 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 122 |\n",
+ "| LossQ | 629 |\n",
+ "| Time | 991 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 41 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 301 |\n",
+ "| MaxEpRet | -182 |\n",
+ "| MinEpRet | -1.31e+03 |\n",
+ "| AverageTestEpRet | -784 |\n",
+ "| StdTestEpRet | 309 |\n",
+ "| MaxTestEpRet | -408 |\n",
+ "| MinTestEpRet | -1.4e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.02e+04 |\n",
+ "| AverageQ1Vals | -136 |\n",
+ "| StdQ1Vals | 197 |\n",
+ "| MaxQ1Vals | 74.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -136 |\n",
+ "| StdQ2Vals | 197 |\n",
+ "| MaxQ2Vals | 74.3 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 121 |\n",
+ "| LossQ | 587 |\n",
+ "| Time | 1.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 42 |\n",
+ "| AverageEpRet | -623 |\n",
+ "| StdEpRet | 295 |\n",
+ "| MaxEpRet | -69.3 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -806 |\n",
+ "| StdTestEpRet | 386 |\n",
+ "| MaxTestEpRet | -285 |\n",
+ "| MinTestEpRet | -1.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.24e+04 |\n",
+ "| AverageQ1Vals | -134 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | 59.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -134 |\n",
+ "| StdQ2Vals | 196 |\n",
+ "| MaxQ2Vals | 67.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 120 |\n",
+ "| LossQ | 584 |\n",
+ "| Time | 1.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 43 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 255 |\n",
+ "| MaxEpRet | -79.6 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -620 |\n",
+ "| StdTestEpRet | 300 |\n",
+ "| MaxTestEpRet | -160 |\n",
+ "| MinTestEpRet | -971 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.46e+04 |\n",
+ "| AverageQ1Vals | -133 |\n",
+ "| StdQ1Vals | 194 |\n",
+ "| MaxQ1Vals | 63.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -133 |\n",
+ "| StdQ2Vals | 194 |\n",
+ "| MaxQ2Vals | 68.2 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 561 |\n",
+ "| Time | 1.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 44 |\n",
+ "| AverageEpRet | -583 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -63.3 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -507 |\n",
+ "| StdTestEpRet | 296 |\n",
+ "| MaxTestEpRet | -107 |\n",
+ "| MinTestEpRet | -996 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.68e+04 |\n",
+ "| AverageQ1Vals | -132 |\n",
+ "| StdQ1Vals | 191 |\n",
+ "| MaxQ1Vals | 49.2 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -132 |\n",
+ "| StdQ2Vals | 191 |\n",
+ "| MaxQ2Vals | 49.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 532 |\n",
+ "| Time | 1.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 45 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 309 |\n",
+ "| MaxEpRet | -147 |\n",
+ "| MinEpRet | -1.23e+03 |\n",
+ "| AverageTestEpRet | -727 |\n",
+ "| StdTestEpRet | 348 |\n",
+ "| MaxTestEpRet | -203 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.9e+04 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 50.6 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 47.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 116 |\n",
+ "| LossQ | 517 |\n",
+ "| Time | 1.14e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 46 |\n",
+ "| AverageEpRet | -649 |\n",
+ "| StdEpRet | 325 |\n",
+ "| MaxEpRet | -151 |\n",
+ "| MinEpRet | -1.5e+03 |\n",
+ "| AverageTestEpRet | -647 |\n",
+ "| StdTestEpRet | 317 |\n",
+ "| MaxTestEpRet | -219 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.01e+05 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 90.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 115 |\n",
+ "| LossQ | 514 |\n",
+ "| Time | 1.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 47 |\n",
+ "| AverageEpRet | -444 |\n",
+ "| StdEpRet | 183 |\n",
+ "| MaxEpRet | -183 |\n",
+ "| MinEpRet | -968 |\n",
+ "| AverageTestEpRet | -669 |\n",
+ "| StdTestEpRet | 245 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.12e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.03e+05 |\n",
+ "| AverageQ1Vals | -127 |\n",
+ "| StdQ1Vals | 185 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -127 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | 55.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 113 |\n",
+ "| LossQ | 489 |\n",
+ "| Time | 1.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 48 |\n",
+ "| AverageEpRet | -673 |\n",
+ "| StdEpRet | 332 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.41e+03 |\n",
+ "| AverageTestEpRet | -534 |\n",
+ "| StdTestEpRet | 252 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.06e+05 |\n",
+ "| AverageQ1Vals | -125 |\n",
+ "| StdQ1Vals | 182 |\n",
+ "| MaxQ1Vals | 96.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -125 |\n",
+ "| StdQ2Vals | 182 |\n",
+ "| MaxQ2Vals | 93.5 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 473 |\n",
+ "| Time | 1.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 49 |\n",
+ "| AverageEpRet | -475 |\n",
+ "| StdEpRet | 243 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -572 |\n",
+ "| StdTestEpRet | 339 |\n",
+ "| MaxTestEpRet | -155 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.08e+05 |\n",
+ "| AverageQ1Vals | -124 |\n",
+ "| StdQ1Vals | 181 |\n",
+ "| MaxQ1Vals | 66.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -124 |\n",
+ "| StdQ2Vals | 181 |\n",
+ "| MaxQ2Vals | 60.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 466 |\n",
+ "| Time | 1.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 50 |\n",
+ "| AverageEpRet | -581 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -121 |\n",
+ "| MinEpRet | -1.34e+03 |\n",
+ "| AverageTestEpRet | -687 |\n",
+ "| StdTestEpRet | 246 |\n",
+ "| MaxTestEpRet | -246 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 50.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 439 |\n",
+ "| Time | 1.31e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 51 |\n",
+ "| AverageEpRet | -599 |\n",
+ "| StdEpRet | 287 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 204 |\n",
+ "| MaxTestEpRet | -216 |\n",
+ "| MinTestEpRet | -856 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.12e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 78.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 76.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 110 |\n",
+ "| LossQ | 437 |\n",
+ "| Time | 1.34e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 52 |\n",
+ "| AverageEpRet | -636 |\n",
+ "| StdEpRet | 283 |\n",
+ "| MaxEpRet | -220 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -712 |\n",
+ "| StdTestEpRet | 185 |\n",
+ "| MaxTestEpRet | -457 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.14e+05 |\n",
+ "| AverageQ1Vals | -121 |\n",
+ "| StdQ1Vals | 178 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -121 |\n",
+ "| StdQ2Vals | 178 |\n",
+ "| MaxQ2Vals | 54.7 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 431 |\n",
+ "| Time | 1.37e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 53 |\n",
+ "| AverageEpRet | -591 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -132 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -551 |\n",
+ "| StdTestEpRet | 206 |\n",
+ "| MaxTestEpRet | -333 |\n",
+ "| MinTestEpRet | -975 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.17e+05 |\n",
+ "| AverageQ1Vals | -119 |\n",
+ "| StdQ1Vals | 176 |\n",
+ "| MaxQ1Vals | 54.4 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -119 |\n",
+ "| StdQ2Vals | 176 |\n",
+ "| MaxQ2Vals | 89.1 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 108 |\n",
+ "| LossQ | 418 |\n",
+ "| Time | 1.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 54 |\n",
+ "| AverageEpRet | -426 |\n",
+ "| StdEpRet | 230 |\n",
+ "| MaxEpRet | -90.2 |\n",
+ "| MinEpRet | -906 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 197 |\n",
+ "| MaxTestEpRet | -124 |\n",
+ "| MinTestEpRet | -708 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.19e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 175 |\n",
+ "| MaxQ1Vals | 47.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 175 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 401 |\n",
+ "| Time | 1.43e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 55 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -939 |\n",
+ "| AverageTestEpRet | -615 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -400 |\n",
+ "| MinTestEpRet | -952 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.21e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 174 |\n",
+ "| MaxQ1Vals | 54.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 174 |\n",
+ "| MaxQ2Vals | 68.6 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 392 |\n",
+ "| Time | 1.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 56 |\n",
+ "| AverageEpRet | -552 |\n",
+ "| StdEpRet | 234 |\n",
+ "| MaxEpRet | -228 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -400 |\n",
+ "| StdTestEpRet | 293 |\n",
+ "| MaxTestEpRet | -66.3 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.23e+05 |\n",
+ "| AverageQ1Vals | -117 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | 66.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -117 |\n",
+ "| StdQ2Vals | 173 |\n",
+ "| MaxQ2Vals | 69.5 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 105 |\n",
+ "| LossQ | 400 |\n",
+ "| Time | 1.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 57 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 318 |\n",
+ "| MaxEpRet | -33.7 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -480 |\n",
+ "| StdTestEpRet | 210 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -802 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.25e+05 |\n",
+ "| AverageQ1Vals | -114 |\n",
+ "| StdQ1Vals | 171 |\n",
+ "| MaxQ1Vals | 68.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -114 |\n",
+ "| StdQ2Vals | 171 |\n",
+ "| MaxQ2Vals | 68.8 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 103 |\n",
+ "| LossQ | 380 |\n",
+ "| Time | 1.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 58 |\n",
+ "| AverageEpRet | -590 |\n",
+ "| StdEpRet | 261 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 234 |\n",
+ "| MaxTestEpRet | -206 |\n",
+ "| MinTestEpRet | -935 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.28e+05 |\n",
+ "| AverageQ1Vals | -113 |\n",
+ "| StdQ1Vals | 170 |\n",
+ "| MaxQ1Vals | 72.9 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -113 |\n",
+ "| StdQ2Vals | 170 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 368 |\n",
+ "| Time | 1.56e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 59 |\n",
+ "| AverageEpRet | -588 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -1.05e+03 |\n",
+ "| AverageTestEpRet | -467 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -763 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.3e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 169 |\n",
+ "| MaxQ1Vals | 64.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 169 |\n",
+ "| MaxQ2Vals | 93.3 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 367 |\n",
+ "| Time | 1.6e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 60 |\n",
+ "| AverageEpRet | -495 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -44.8 |\n",
+ "| MinEpRet | -1.71e+03 |\n",
+ "| AverageTestEpRet | -654 |\n",
+ "| StdTestEpRet | 261 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.05e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 168 |\n",
+ "| MaxQ1Vals | 68.9 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 168 |\n",
+ "| MaxQ2Vals | 73.7 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 101 |\n",
+ "| LossQ | 363 |\n",
+ "| Time | 1.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 61 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 376 |\n",
+ "| MaxEpRet | -74.9 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -574 |\n",
+ "| StdTestEpRet | 410 |\n",
+ "| MaxTestEpRet | -144 |\n",
+ "| MinTestEpRet | -1.57e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.34e+05 |\n",
+ "| AverageQ1Vals | -110 |\n",
+ "| StdQ1Vals | 166 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -110 |\n",
+ "| StdQ2Vals | 166 |\n",
+ "| MaxQ2Vals | 52.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 100 |\n",
+ "| LossQ | 349 |\n",
+ "| Time | 1.66e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 62 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -485 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -886 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.36e+05 |\n",
+ "| AverageQ1Vals | -109 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 67.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -109 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 98.7 |\n",
+ "| LossQ | 358 |\n",
+ "| Time | 1.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 63 |\n",
+ "| AverageEpRet | -448 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -56.4 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -341 |\n",
+ "| StdTestEpRet | 181 |\n",
+ "| MaxTestEpRet | -63.6 |\n",
+ "| MinTestEpRet | -608 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.39e+05 |\n",
+ "| AverageQ1Vals | -108 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -108 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 52.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 97.8 |\n",
+ "| LossQ | 344 |\n",
+ "| Time | 1.73e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 64 |\n",
+ "| AverageEpRet | -525 |\n",
+ "| StdEpRet | 226 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -532 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -302 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.41e+05 |\n",
+ "| AverageQ1Vals | -106 |\n",
+ "| StdQ1Vals | 164 |\n",
+ "| MaxQ1Vals | 50.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -106 |\n",
+ "| StdQ2Vals | 164 |\n",
+ "| MaxQ2Vals | 37.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 95.7 |\n",
+ "| LossQ | 340 |\n",
+ "| Time | 1.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 65 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -31.2 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -520 |\n",
+ "| StdTestEpRet | 359 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -1.51e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.43e+05 |\n",
+ "| AverageQ1Vals | -105 |\n",
+ "| StdQ1Vals | 162 |\n",
+ "| MaxQ1Vals | 58.3 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -105 |\n",
+ "| StdQ2Vals | 162 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 95.2 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 66 |\n",
+ "| AverageEpRet | -622 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -126 |\n",
+ "| MinEpRet | -1.33e+03 |\n",
+ "| AverageTestEpRet | -525 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.45e+05 |\n",
+ "| AverageQ1Vals | -104 |\n",
+ "| StdQ1Vals | 163 |\n",
+ "| MaxQ1Vals | 46.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -104 |\n",
+ "| StdQ2Vals | 163 |\n",
+ "| MaxQ2Vals | 73.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 94.4 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.83e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 67 |\n",
+ "| AverageEpRet | -483 |\n",
+ "| StdEpRet | 204 |\n",
+ "| MaxEpRet | -118 |\n",
+ "| MinEpRet | -836 |\n",
+ "| AverageTestEpRet | -530 |\n",
+ "| StdTestEpRet | 231 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -994 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.47e+05 |\n",
+ "| AverageQ1Vals | -103 |\n",
+ "| StdQ1Vals | 161 |\n",
+ "| MaxQ1Vals | 56 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -103 |\n",
+ "| StdQ2Vals | 161 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 93.2 |\n",
+ "| LossQ | 325 |\n",
+ "| Time | 1.86e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 68 |\n",
+ "| AverageEpRet | -443 |\n",
+ "| StdEpRet | 266 |\n",
+ "| MaxEpRet | -17.7 |\n",
+ "| MinEpRet | -926 |\n",
+ "| AverageTestEpRet | -498 |\n",
+ "| StdTestEpRet | 213 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -779 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.5e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 71.7 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 76.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.3 |\n",
+ "| LossQ | 311 |\n",
+ "| Time | 1.89e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 69 |\n",
+ "| AverageEpRet | -542 |\n",
+ "| StdEpRet | 422 |\n",
+ "| MaxEpRet | -74.6 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 205 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -905 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.52e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 57.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 106 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.1 |\n",
+ "| LossQ | 315 |\n",
+ "| Time | 1.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 70 |\n",
+ "| AverageEpRet | -556 |\n",
+ "| StdEpRet | 188 |\n",
+ "| MaxEpRet | -166 |\n",
+ "| MinEpRet | -827 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -247 |\n",
+ "| MinTestEpRet | -782 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 104 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 89.6 |\n",
+ "| LossQ | 298 |\n",
+ "| Time | 1.96e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 71 |\n",
+ "| AverageEpRet | -508 |\n",
+ "| StdEpRet | 360 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.65e+03 |\n",
+ "| AverageTestEpRet | -503 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -98.4 |\n",
+ "| MinTestEpRet | -933 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.56e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 68.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 62.4 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.9 |\n",
+ "| LossQ | 310 |\n",
+ "| Time | 1.98e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 72 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -154 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 299 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.58e+05 |\n",
+ "| AverageQ1Vals | -98.4 |\n",
+ "| StdQ1Vals | 157 |\n",
+ "| MaxQ1Vals | 40.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -98.4 |\n",
+ "| StdQ2Vals | 157 |\n",
+ "| MaxQ2Vals | 65.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 307 |\n",
+ "| Time | 2.01e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 73 |\n",
+ "| AverageEpRet | -531 |\n",
+ "| StdEpRet | 253 |\n",
+ "| MaxEpRet | -152 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -403 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -66.2 |\n",
+ "| MinTestEpRet | -865 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.61e+05 |\n",
+ "| AverageQ1Vals | -98 |\n",
+ "| StdQ1Vals | 156 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -98 |\n",
+ "| StdQ2Vals | 156 |\n",
+ "| MaxQ2Vals | 61.2 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 290 |\n",
+ "| Time | 2.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 74 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 225 |\n",
+ "| MaxEpRet | -242 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -421 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -972 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.63e+05 |\n",
+ "| AverageQ1Vals | -96.8 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 84.5 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -96.8 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 44 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 87.6 |\n",
+ "| LossQ | 280 |\n",
+ "| Time | 2.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 75 |\n",
+ "| AverageEpRet | -438 |\n",
+ "| StdEpRet | 207 |\n",
+ "| MaxEpRet | -103 |\n",
+ "| MinEpRet | -930 |\n",
+ "| AverageTestEpRet | -416 |\n",
+ "| StdTestEpRet | 241 |\n",
+ "| MaxTestEpRet | -135 |\n",
+ "| MinTestEpRet | -887 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.65e+05 |\n",
+ "| AverageQ1Vals | -96.7 |\n",
+ "| StdQ1Vals | 155 |\n",
+ "| MaxQ1Vals | 72.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -96.7 |\n",
+ "| StdQ2Vals | 155 |\n",
+ "| MaxQ2Vals | 75.7 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 88.1 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 76 |\n",
+ "| AverageEpRet | -639 |\n",
+ "| StdEpRet | 315 |\n",
+ "| MaxEpRet | -214 |\n",
+ "| MinEpRet | -1.51e+03 |\n",
+ "| AverageTestEpRet | -437 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -19.2 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.67e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 48.5 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 46.2 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 86.8 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.14e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 77 |\n",
+ "| AverageEpRet | -468 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -901 |\n",
+ "| AverageTestEpRet | -556 |\n",
+ "| StdTestEpRet | 262 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -812 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.69e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 37.2 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 49.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.9 |\n",
+ "| LossQ | 283 |\n",
+ "| Time | 2.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 78 |\n",
+ "| AverageEpRet | -568 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -123 |\n",
+ "| MinEpRet | -1.27e+03 |\n",
+ "| AverageTestEpRet | -550 |\n",
+ "| StdTestEpRet | 360 |\n",
+ "| MaxTestEpRet | -164 |\n",
+ "| MinTestEpRet | -1.26e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.72e+05 |\n",
+ "| AverageQ1Vals | -94.5 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 47.8 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -94.5 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 92 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 85.9 |\n",
+ "| LossQ | 265 |\n",
+ "| Time | 2.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 79 |\n",
+ "| AverageEpRet | -566 |\n",
+ "| StdEpRet | 337 |\n",
+ "| MaxEpRet | -96.5 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -543 |\n",
+ "| StdTestEpRet | 365 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.74e+05 |\n",
+ "| AverageQ1Vals | -94.8 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 69.7 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -94.8 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 75.6 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 278 |\n",
+ "| Time | 2.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 80 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 290 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 196 |\n",
+ "| MaxTestEpRet | -182 |\n",
+ "| MinTestEpRet | -783 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+05 |\n",
+ "| AverageQ1Vals | -94.6 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 59.6 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -94.6 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 74.6 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 271 |\n",
+ "| Time | 2.28e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 81 |\n",
+ "| AverageEpRet | -535 |\n",
+ "| StdEpRet | 217 |\n",
+ "| MaxEpRet | -131 |\n",
+ "| MinEpRet | -999 |\n",
+ "| AverageTestEpRet | -422 |\n",
+ "| StdTestEpRet | 270 |\n",
+ "| MaxTestEpRet | -67.3 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.78e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 150 |\n",
+ "| MaxQ1Vals | 47.4 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 150 |\n",
+ "| MaxQ2Vals | 71 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 85.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.31e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 82 |\n",
+ "| AverageEpRet | -505 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -79.9 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -734 |\n",
+ "| StdTestEpRet | 289 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.22e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.8e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 149 |\n",
+ "| MaxQ1Vals | 53 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 149 |\n",
+ "| MaxQ2Vals | 67.3 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 86.6 |\n",
+ "| LossQ | 267 |\n",
+ "| Time | 2.35e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 83 |\n",
+ "| AverageEpRet | -585 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -96.7 |\n",
+ "| MinEpRet | -1.3e+03 |\n",
+ "| AverageTestEpRet | -436 |\n",
+ "| StdTestEpRet | 169 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -748 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.83e+05 |\n",
+ "| AverageQ1Vals | -92.9 |\n",
+ "| StdQ1Vals | 147 |\n",
+ "| MaxQ1Vals | 44.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -92.9 |\n",
+ "| StdQ2Vals | 147 |\n",
+ "| MaxQ2Vals | 75.2 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 84.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.38e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 84 |\n",
+ "| AverageEpRet | -406 |\n",
+ "| StdEpRet | 232 |\n",
+ "| MaxEpRet | -128 |\n",
+ "| MinEpRet | -924 |\n",
+ "| AverageTestEpRet | -650 |\n",
+ "| StdTestEpRet | 236 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.85e+05 |\n",
+ "| AverageQ1Vals | -92.5 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 46.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -92.5 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 80.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 84.4 |\n",
+ "| LossQ | 258 |\n",
+ "| Time | 2.41e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 85 |\n",
+ "| AverageEpRet | -474 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -180 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -527 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -166 |\n",
+ "| MinTestEpRet | -737 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.87e+05 |\n",
+ "| AverageQ1Vals | -91.8 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 51.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -91.8 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 33.2 |\n",
+ "| MinQ2Vals | -1.06e+03 |\n",
+ "| LossPi | 84.1 |\n",
+ "| LossQ | 249 |\n",
+ "| Time | 2.44e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 86 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 264 |\n",
+ "| MaxEpRet | -112 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -665 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.89e+05 |\n",
+ "| AverageQ1Vals | -91.6 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 65.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -91.7 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 67.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 83.5 |\n",
+ "| LossQ | 238 |\n",
+ "| Time | 2.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 87 |\n",
+ "| AverageEpRet | -515 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -122 |\n",
+ "| MinEpRet | -976 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 202 |\n",
+ "| MaxTestEpRet | -180 |\n",
+ "| MinTestEpRet | -829 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.91e+05 |\n",
+ "| AverageQ1Vals | -92.1 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -92.1 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 54.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 83.6 |\n",
+ "| LossQ | 256 |\n",
+ "| Time | 2.51e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 88 |\n",
+ "| AverageEpRet | -572 |\n",
+ "| StdEpRet | 258 |\n",
+ "| MaxEpRet | -57 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -508 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -217 |\n",
+ "| MinTestEpRet | -794 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.94e+05 |\n",
+ "| AverageQ1Vals | -91.3 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 42.6 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -91.3 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 58 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 83.7 |\n",
+ "| LossQ | 246 |\n",
+ "| Time | 2.54e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 89 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 241 |\n",
+ "| MaxEpRet | -49.9 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -562 |\n",
+ "| StdTestEpRet | 306 |\n",
+ "| MaxTestEpRet | -68.2 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.96e+05 |\n",
+ "| AverageQ1Vals | -90.3 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -90.3 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 54.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 83 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.57e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 90 |\n",
+ "| AverageEpRet | -478 |\n",
+ "| StdEpRet | 286 |\n",
+ "| MaxEpRet | -68 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -603 |\n",
+ "| StdTestEpRet | 390 |\n",
+ "| MaxTestEpRet | -123 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.6 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.6e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 91 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 252 |\n",
+ "| MaxEpRet | -101 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -566 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -62.9 |\n",
+ "| MinTestEpRet | -1.02e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 25.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.7 |\n",
+ "| LossQ | 241 |\n",
+ "| Time | 2.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 92 |\n",
+ "| AverageEpRet | -425 |\n",
+ "| StdEpRet | 205 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -998 |\n",
+ "| AverageTestEpRet | -536 |\n",
+ "| StdTestEpRet | 271 |\n",
+ "| MaxTestEpRet | -209 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.02e+05 |\n",
+ "| AverageQ1Vals | -88.7 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -88.7 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 32.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 81.3 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.67e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 93 |\n",
+ "| AverageEpRet | -498 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -83.8 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -523 |\n",
+ "| StdTestEpRet | 286 |\n",
+ "| MaxTestEpRet | -83.5 |\n",
+ "| MinTestEpRet | -967 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.05e+05 |\n",
+ "| AverageQ1Vals | -88.2 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 97.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -88.2 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 36.8 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 80.8 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 94 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 231 |\n",
+ "| MaxEpRet | -88.9 |\n",
+ "| MinEpRet | -946 |\n",
+ "| AverageTestEpRet | -407 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -793 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.07e+05 |\n",
+ "| AverageQ1Vals | -87.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -87.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.6 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 80.4 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.73e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 95 |\n",
+ "| AverageEpRet | -471 |\n",
+ "| StdEpRet | 257 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -994 |\n",
+ "| AverageTestEpRet | -563 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -827 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.09e+05 |\n",
+ "| AverageQ1Vals | -87.2 |\n",
+ "| StdQ1Vals | 141 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -87.2 |\n",
+ "| StdQ2Vals | 141 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 80.3 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 96 |\n",
+ "| AverageEpRet | -473 |\n",
+ "| StdEpRet | 268 |\n",
+ "| MaxEpRet | -76.1 |\n",
+ "| MinEpRet | -1.03e+03 |\n",
+ "| AverageTestEpRet | -392 |\n",
+ "| StdTestEpRet | 191 |\n",
+ "| MaxTestEpRet | -90.2 |\n",
+ "| MinTestEpRet | -713 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.11e+05 |\n",
+ "| AverageQ1Vals | -86.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 32.9 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -86.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 40.5 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 79.5 |\n",
+ "| LossQ | 233 |\n",
+ "| Time | 2.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 97 |\n",
+ "| AverageEpRet | -497 |\n",
+ "| StdEpRet | 345 |\n",
+ "| MaxEpRet | -37.1 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 227 |\n",
+ "| MaxTestEpRet | -72.6 |\n",
+ "| MinTestEpRet | -742 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.13e+05 |\n",
+ "| AverageQ1Vals | -86.5 |\n",
+ "| StdQ1Vals | 140 |\n",
+ "| MaxQ1Vals | 43 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -86.5 |\n",
+ "| StdQ2Vals | 140 |\n",
+ "| MaxQ2Vals | 38.9 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 79.6 |\n",
+ "| LossQ | 234 |\n",
+ "| Time | 2.82e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 98 |\n",
+ "| AverageEpRet | -429 |\n",
+ "| StdEpRet | 215 |\n",
+ "| MaxEpRet | -135 |\n",
+ "| MinEpRet | -848 |\n",
+ "| AverageTestEpRet | -511 |\n",
+ "| StdTestEpRet | 337 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -1.37e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.16e+05 |\n",
+ "| AverageQ1Vals | -86.1 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -86.1 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43.7 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 79.1 |\n",
+ "| LossQ | 226 |\n",
+ "| Time | 2.85e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 99 |\n",
+ "| AverageEpRet | -514 |\n",
+ "| StdEpRet | 278 |\n",
+ "| MaxEpRet | -141 |\n",
+ "| MinEpRet | -1.21e+03 |\n",
+ "| AverageTestEpRet | -633 |\n",
+ "| StdTestEpRet | 489 |\n",
+ "| MaxTestEpRet | -181 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.18e+05 |\n",
+ "| AverageQ1Vals | -84.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -84.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 27.5 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 77.7 |\n",
+ "| LossQ | 225 |\n",
+ "| Time | 2.88e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 100 |\n",
+ "| AverageEpRet | -557 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -26 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -544 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -1.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+05 |\n",
+ "| AverageQ1Vals | -84 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 51.4 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -84 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43 |\n",
+ "| MinQ2Vals | -1.22e+03 |\n",
+ "| LossPi | 76.6 |\n",
+ "| LossQ | 220 |\n",
+ "| Time | 2.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 101 |\n",
+ "| AverageEpRet | -434 |\n",
+ "| StdEpRet | 192 |\n",
+ "| MaxEpRet | -155 |\n",
+ "| MinEpRet | -806 |\n",
+ "| AverageTestEpRet | -418 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -244 |\n",
+ "| MinTestEpRet | -730 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.22e+05 |\n",
+ "| AverageQ1Vals | -82.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 35.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -82.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 54.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 76.3 |\n",
+ "| LossQ | 219 |\n",
+ "| Time | 2.95e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 102 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -120 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -490 |\n",
+ "| StdTestEpRet | 223 |\n",
+ "| MaxTestEpRet | -73.4 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.24e+05 |\n",
+ "| AverageQ1Vals | -81.2 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 96.8 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -81.2 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 71.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 74.2 |\n",
+ "| LossQ | 213 |\n",
+ "| Time | 2.99e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 103 |\n",
+ "| AverageEpRet | -617 |\n",
+ "| StdEpRet | 327 |\n",
+ "| MaxEpRet | -104 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 214 |\n",
+ "| MaxTestEpRet | -248 |\n",
+ "| MinTestEpRet | -906 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.27e+05 |\n",
+ "| AverageQ1Vals | -80.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 70.5 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -80.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 41 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 74.1 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 104 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -65.7 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -425 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -774 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.29e+05 |\n",
+ "| AverageQ1Vals | -79.9 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 41.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -79.9 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 44.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 73.2 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 105 |\n",
+ "| AverageEpRet | -469 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -144 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -317 |\n",
+ "| StdTestEpRet | 137 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -541 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.31e+05 |\n",
+ "| AverageQ1Vals | -78.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 24.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -78.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 38.7 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.6 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 106 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 244 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -989 |\n",
+ "| AverageTestEpRet | -542 |\n",
+ "| StdTestEpRet | 277 |\n",
+ "| MaxTestEpRet | -129 |\n",
+ "| MinTestEpRet | -965 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.33e+05 |\n",
+ "| AverageQ1Vals | -79.1 |\n",
+ "| StdQ1Vals | 136 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -79.1 |\n",
+ "| StdQ2Vals | 136 |\n",
+ "| MaxQ2Vals | 54 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 72.7 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 107 |\n",
+ "| AverageEpRet | -571 |\n",
+ "| StdEpRet | 245 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -538 |\n",
+ "| StdTestEpRet | 189 |\n",
+ "| MaxTestEpRet | -255 |\n",
+ "| MinTestEpRet | -880 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.35e+05 |\n",
+ "| AverageQ1Vals | -78.4 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 33.8 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -78.4 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 71.6 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.15e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 108 |\n",
+ "| AverageEpRet | -481 |\n",
+ "| StdEpRet | 187 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -837 |\n",
+ "| AverageTestEpRet | -399 |\n",
+ "| StdTestEpRet | 162 |\n",
+ "| MaxTestEpRet | -149 |\n",
+ "| MinTestEpRet | -668 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.38e+05 |\n",
+ "| AverageQ1Vals | -78 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 34 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -78 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.1 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.18e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 109 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 272 |\n",
+ "| MaxEpRet | -63.6 |\n",
+ "| MinEpRet | -1.04e+03 |\n",
+ "| AverageTestEpRet | -531 |\n",
+ "| StdTestEpRet | 326 |\n",
+ "| MaxTestEpRet | -90.1 |\n",
+ "| MinTestEpRet | -1.19e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.4e+05 |\n",
+ "| AverageQ1Vals | -76.9 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 59 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.9 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 42.2 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.7 |\n",
+ "| LossQ | 212 |\n",
+ "| Time | 3.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 110 |\n",
+ "| AverageEpRet | -523 |\n",
+ "| StdEpRet | 321 |\n",
+ "| MaxEpRet | -29.2 |\n",
+ "| MinEpRet | -1.39e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 282 |\n",
+ "| MaxTestEpRet | -187 |\n",
+ "| MinTestEpRet | -1.03e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+05 |\n",
+ "| AverageQ1Vals | -76.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 43.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 53.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 70.9 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 111 |\n",
+ "| AverageEpRet | -453 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -81.5 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -524 |\n",
+ "| StdTestEpRet | 105 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -689 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.44e+05 |\n",
+ "| AverageQ1Vals | -76.5 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 30.1 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.5 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.8 |\n",
+ "| MinQ2Vals | -1.23e+03 |\n",
+ "| LossPi | 70.4 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 112 |\n",
+ "| AverageEpRet | -596 |\n",
+ "| StdEpRet | 262 |\n",
+ "| MaxEpRet | -173 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -415 |\n",
+ "| StdTestEpRet | 161 |\n",
+ "| MaxTestEpRet | -137 |\n",
+ "| MinTestEpRet | -652 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.46e+05 |\n",
+ "| AverageQ1Vals | -76.3 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 34.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.3 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 46.7 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.3 |\n",
+ "| LossQ | 198 |\n",
+ "| Time | 3.3e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 113 |\n",
+ "| AverageEpRet | -482 |\n",
+ "| StdEpRet | 333 |\n",
+ "| MaxEpRet | -62.8 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -559 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -142 |\n",
+ "| MinTestEpRet | -1.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.49e+05 |\n",
+ "| AverageQ1Vals | -76.2 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 38.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -76.2 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 50.3 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.33e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 114 |\n",
+ "| AverageEpRet | -450 |\n",
+ "| StdEpRet | 216 |\n",
+ "| MaxEpRet | -94.9 |\n",
+ "| MinEpRet | -872 |\n",
+ "| AverageTestEpRet | -432 |\n",
+ "| StdTestEpRet | 132 |\n",
+ "| MaxTestEpRet | -232 |\n",
+ "| MinTestEpRet | -645 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.51e+05 |\n",
+ "| AverageQ1Vals | -75.7 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 44.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -75.7 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 69.6 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.36e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 115 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 240 |\n",
+ "| MaxEpRet | -108 |\n",
+ "| MinEpRet | -932 |\n",
+ "| AverageTestEpRet | -371 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -143 |\n",
+ "| MinTestEpRet | -616 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.53e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 21.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 29.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 205 |\n",
+ "| Time | 3.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 116 |\n",
+ "| AverageEpRet | -538 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -960 |\n",
+ "| AverageTestEpRet | -475 |\n",
+ "| StdTestEpRet | 311 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -1.23e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.55e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 26 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 37.1 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 69.3 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.43e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 117 |\n",
+ "| AverageEpRet | -437 |\n",
+ "| StdEpRet | 203 |\n",
+ "| MaxEpRet | -92.8 |\n",
+ "| MinEpRet | -857 |\n",
+ "| AverageTestEpRet | -582 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -79.8 |\n",
+ "| MinTestEpRet | -861 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.57e+05 |\n",
+ "| AverageQ1Vals | -75.5 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 49.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -75.5 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 51 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 69.4 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.47e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 118 |\n",
+ "| AverageEpRet | -549 |\n",
+ "| StdEpRet | 236 |\n",
+ "| MaxEpRet | -111 |\n",
+ "| MinEpRet | -1.28e+03 |\n",
+ "| AverageTestEpRet | -588 |\n",
+ "| StdTestEpRet | 404 |\n",
+ "| MaxTestEpRet | -138 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.6e+05 |\n",
+ "| AverageQ1Vals | -75.1 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 31.7 |\n",
+ "| MinQ1Vals | -1.05e+03 |\n",
+ "| AverageQ2Vals | -75.1 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 38.6 |\n",
+ "| MinQ2Vals | -1.05e+03 |\n",
+ "| LossPi | 69 |\n",
+ "| LossQ | 192 |\n",
+ "| Time | 3.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 119 |\n",
+ "| AverageEpRet | -491 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -102 |\n",
+ "| MinEpRet | -945 |\n",
+ "| AverageTestEpRet | -535 |\n",
+ "| StdTestEpRet | 217 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -879 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.62e+05 |\n",
+ "| AverageQ1Vals | -74.9 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 60.9 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.9 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 28.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 69.2 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 120 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 249 |\n",
+ "| MaxEpRet | -64.6 |\n",
+ "| MinEpRet | -885 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 242 |\n",
+ "| MaxTestEpRet | -56.1 |\n",
+ "| MinTestEpRet | -838 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+05 |\n",
+ "| AverageQ1Vals | -74.8 |\n",
+ "| StdQ1Vals | 129 |\n",
+ "| MaxQ1Vals | 105 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.8 |\n",
+ "| StdQ2Vals | 129 |\n",
+ "| MaxQ2Vals | 77.4 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 69.1 |\n",
+ "| LossQ | 197 |\n",
+ "| Time | 3.56e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ " parser = argparse.ArgumentParser()\n",
+ " parser.add_argument('--cpu', type=int, default=4)\n",
+ " parser.add_argument('--num_runs', type=int, default=3)\n",
+ " args = parser.parse_args()\n",
+ "\n",
+ " eg = ExperimentGrid(name='ppo-pyt-bench')\n",
+ " eg.add('env_name', 'CartPole-v0', '', True)\n",
+ " eg.add('seed', [10*i for i in range(args.num_runs)])\n",
+ " eg.add('epochs', 10)\n",
+ " eg.add('steps_per_epoch', 4000)\n",
+ " eg.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')\n",
+ " eg.add('ac_kwargs:activation', [torch.nn.Tanh, torch.nn.ReLU], '')\n",
+ " eg.run(ppo_pytorch, num_cpu=args.cpu)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Gn3Gp40bcOVz"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 972,
+ "status": "ok",
+ "timestamp": 1584036455886,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "6GyY0wE-QBOj",
+ "outputId": "9a5e9011-e024-4a65-8383-83c062cdcad9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-900.18321242575\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "env = GyroscopeEnv()\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = torch.load('model/pyt_save/model.pt')\n",
+ "\n",
+ "# Test parameters\n",
+ "x1,x2,x3,x4,x1_ref,x3_ref,w = 0,1,0,1,1,3,25\n",
+ "state = env.reset(np.array([x1,x2,x3,x4,x1_ref,x3_ref,w]))\n",
+ "val = []\n",
+ "act = []\n",
+ "dt = 0.01\n",
+ "time = np.arange(0, 4, dt)\n",
+ "score = 0\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " action = agent.act(torch.as_tensor(state, dtype=torch.float32))\n",
+ " act.append(action)\n",
+ " state, reward, done, _ = env.step(action)\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ "\n",
+ "env.close()\n",
+ "print(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "vvMjuRHDcfrE"
+ },
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 1856,
+ "status": "ok",
+ "timestamp": 1584036457424,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "aCZCqujgcMVA",
+ "outputId": "05490294-aab8-4933-ca9b-e13ba85ecf6d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2160x2160 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(4,2,figsize=(30,30))\n",
+ "plt.subplot(4,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "plt.plot(time, [row[4] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,2)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "plt.plot(time, [row[5] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,3)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[0]- row[4]) for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[2]- row[5]) for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,7)\n",
+ "plt.title('Red gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u1 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in act],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,8)\n",
+ "plt.title('Blue gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u2 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in act],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fz9r-gaStzpk"
+ },
+ "source": [
+ "## 3D rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "HQM1E8JYc-cC"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 400\n",
+ "\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "gyroscope_ddpg_testing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/training_spinuplib/gyroscope_training_spinuplib.ipynb b/code/training_spinuplib/gyroscope_training_spinuplib.ipynb
new file mode 100644
index 0000000..56b6d87
--- /dev/null
+++ b/code/training_spinuplib/gyroscope_training_spinuplib.ipynb
@@ -0,0 +1,3800 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "x83dMPapQBN6"
+ },
+ "source": [
+ "# Gyroscope DDPG/TD3/SAC training (spinup library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8inmkEG5QHqx"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 655,
+ "status": "ok",
+ "timestamp": 1584030129316,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "DnnJGcnsQvZC",
+ "outputId": "9d6d1408-da29-44ed-af74-4ebdc2bd865a"
+ },
+ "outputs": [],
+ "source": [
+ "cd drive/My\\ Drive/Colab Notebooks/DDPG_uda_v0/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "fuJhdd479TpP"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'spinup'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-128c06fd51b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mvpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mspinup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'spinup'"
+ ]
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from collections import deque\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "from vpython import *\n",
+ "\n",
+ "import spinup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "O0O0t5ZR9Tp6"
+ },
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Al_k1rtvQBOM"
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self):\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = 0.05\n",
+ " \n",
+ " # Error\n",
+ " self.int_diff_x1 = 0\n",
+ " self.int_diff_x3 = 0\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 100 * 2 * np.pi / 60\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiskSpeed = 300 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxAngle,self.maxAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, x1_ref, x3_ref, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " # Angle error\n",
+ " diff_x1 = angle_normalize(x1 - x1_ref)\n",
+ " diff_x3 = angle_normalize(x3 - x3_ref)\n",
+ " \n",
+ " # Integral of error\n",
+ " self.int_diff_x1 = self.int_diff_x1 + diff_x1\n",
+ " self.int_diff_x3 = self.int_diff_x3 + diff_x3\n",
+ " \n",
+ " # Reward 1: differentiable reward (LQR obj function)\n",
+ " reward = -((3*diff_x1)**2 + (3*diff_x3)**2 + (.2*x2)**2 + (.2*x4)**2 + (.1*u1)**2 + (.1*u2)**2)\\\n",
+ " #-(0.01*abs(self.int_diff_x1) + 0.01*abs(self.int_diff_x3))\n",
+ "\n",
+ " \"\"\"# Count time spent in goal:\n",
+ " if abs(diff_x1)<0.05 and abs(diff_x3)<0.05:\n",
+ " self.countGoal +=1\n",
+ " else:\n",
+ " self.countGoal = 0\n",
+ " \n",
+ " # Reward 2: sparse reward for staying in goal range for a long time \n",
+ " if self.countGoal >= (self.timeGoal)/self.dt: #max expected reward over length becomes 0 + (totaltime-goaltime)\n",
+ " reward += 1\"\"\"\n",
+ "\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,x1_ref, x3_ref,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, state = None):\n",
+ " \n",
+ " \n",
+ " # Generate random state (for training) or use given state (for simulation)\n",
+ " if state is None:\n",
+ " self.state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " self.state = state\n",
+ "\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = x[-1]\n",
+ "\n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "be0wYIeBQBOc"
+ },
+ "source": [
+ "## Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 533
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 654004,
+ "status": "error",
+ "timestamp": 1584037207187,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "fLyFHs0yQBOd",
+ "outputId": "260489ff-5e40-416a-e529-5a0cfcaefceb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Log dir model already exists! Storing info there anyway.\n",
+ "\u001b[32;1mLogging data to model/progress.txt\u001b[0m\n",
+ "\u001b[36;1mSaving config:\n",
+ "\u001b[0m\n",
+ "{\n",
+ " \"ac_kwargs\":\t{},\n",
+ " \"act_noise\":\t0.1,\n",
+ " \"actor_critic\":\t\"MLPActorCritic\",\n",
+ " \"batch_size\":\t100,\n",
+ " \"env_fn\":\t\"GyroscopeEnv\",\n",
+ " \"epochs\":\t120,\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"gamma\":\t0.95,\n",
+ " \"logger\":\t{\n",
+ " \"<spinup.utils.logx.EpochLogger object at 0x7f7e800ca588>\":\t{\n",
+ " \"epoch_dict\":\t{},\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"first_row\":\ttrue,\n",
+ " \"log_current_row\":\t{},\n",
+ " \"log_headers\":\t[],\n",
+ " \"output_dir\":\t\"model\",\n",
+ " \"output_file\":\t{\n",
+ " \"<_io.TextIOWrapper name='model/progress.txt' mode='w' encoding='UTF-8'>\":\t{\n",
+ " \"mode\":\t\"w\"\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " },\n",
+ " \"logger_kwargs\":\t{\n",
+ " \"exp_name\":\t\"v0\",\n",
+ " \"output_dir\":\t\"model\"\n",
+ " },\n",
+ " \"max_ep_len\":\t110,\n",
+ " \"noise_clip\":\t0.5,\n",
+ " \"num_test_episodes\":\t10,\n",
+ " \"pi_lr\":\t0.001,\n",
+ " \"policy_delay\":\t2,\n",
+ " \"polyak\":\t0.995,\n",
+ " \"q_lr\":\t0.001,\n",
+ " \"replay_size\":\t1000000,\n",
+ " \"save_freq\":\t1,\n",
+ " \"seed\":\t0,\n",
+ " \"start_steps\":\t20000,\n",
+ " \"steps_per_epoch\":\t2200,\n",
+ " \"target_noise\":\t0.2,\n",
+ " \"update_after\":\t800,\n",
+ " \"update_every\":\t50\n",
+ "}\n",
+ "\u001b[32;1m\n",
+ "Number of parameters: \t pi: 68354, \t q1: 68609, \t q2: 68609\n",
+ "\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 1 |\n",
+ "| AverageEpRet | -6.44e+03 |\n",
+ "| StdEpRet | 1.49e+03 |\n",
+ "| MaxEpRet | -4.55e+03 |\n",
+ "| MinEpRet | -9.52e+03 |\n",
+ "| AverageTestEpRet | -7.13e+03 |\n",
+ "| StdTestEpRet | 1.25e+03 |\n",
+ "| MaxTestEpRet | -4.01e+03 |\n",
+ "| MinTestEpRet | -8.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+03 |\n",
+ "| AverageQ1Vals | -123 |\n",
+ "| StdQ1Vals | 54.5 |\n",
+ "| MaxQ1Vals | 0.0809 |\n",
+ "| MinQ1Vals | -364 |\n",
+ "| AverageQ2Vals | -123 |\n",
+ "| StdQ2Vals | 54.6 |\n",
+ "| MaxQ2Vals | 1.77 |\n",
+ "| MinQ2Vals | -373 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 1.95e+03 |\n",
+ "| Time | 15.8 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 2 |\n",
+ "| AverageEpRet | -6.72e+03 |\n",
+ "| StdEpRet | 1.81e+03 |\n",
+ "| MaxEpRet | -4.58e+03 |\n",
+ "| MinEpRet | -1.07e+04 |\n",
+ "| AverageTestEpRet | -8.21e+03 |\n",
+ "| StdTestEpRet | 1.1e+03 |\n",
+ "| MaxTestEpRet | -6.46e+03 |\n",
+ "| MinTestEpRet | -1.01e+04 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+03 |\n",
+ "| AverageQ1Vals | -276 |\n",
+ "| StdQ1Vals | 98.9 |\n",
+ "| MaxQ1Vals | -54.4 |\n",
+ "| MinQ1Vals | -672 |\n",
+ "| AverageQ2Vals | -276 |\n",
+ "| StdQ2Vals | 99.2 |\n",
+ "| MaxQ2Vals | -51.6 |\n",
+ "| MinQ2Vals | -686 |\n",
+ "| LossPi | 256 |\n",
+ "| LossQ | 2.12e+03 |\n",
+ "| Time | 42.1 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 3 |\n",
+ "| AverageEpRet | -7.08e+03 |\n",
+ "| StdEpRet | 1.22e+03 |\n",
+ "| MaxEpRet | -4.94e+03 |\n",
+ "| MinEpRet | -9.27e+03 |\n",
+ "| AverageTestEpRet | -5.91e+03 |\n",
+ "| StdTestEpRet | 1.83e+03 |\n",
+ "| MaxTestEpRet | -3.79e+03 |\n",
+ "| MinTestEpRet | -9.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+03 |\n",
+ "| AverageQ1Vals | -414 |\n",
+ "| StdQ1Vals | 128 |\n",
+ "| MaxQ1Vals | -36.3 |\n",
+ "| MinQ1Vals | -952 |\n",
+ "| AverageQ2Vals | -414 |\n",
+ "| StdQ2Vals | 128 |\n",
+ "| MaxQ2Vals | -26.8 |\n",
+ "| MinQ2Vals | -950 |\n",
+ "| LossPi | 382 |\n",
+ "| LossQ | 2.76e+03 |\n",
+ "| Time | 67.9 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 4 |\n",
+ "| AverageEpRet | -7.06e+03 |\n",
+ "| StdEpRet | 1.53e+03 |\n",
+ "| MaxEpRet | -4.98e+03 |\n",
+ "| MinEpRet | -1.09e+04 |\n",
+ "| AverageTestEpRet | -5.01e+03 |\n",
+ "| StdTestEpRet | 1.18e+03 |\n",
+ "| MaxTestEpRet | -3.34e+03 |\n",
+ "| MinTestEpRet | -6.44e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+03 |\n",
+ "| AverageQ1Vals | -501 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 11.2 |\n",
+ "| MinQ1Vals | -1.07e+03 |\n",
+ "| AverageQ2Vals | -501 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 16.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 459 |\n",
+ "| LossQ | 3.94e+03 |\n",
+ "| Time | 92 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 5 |\n",
+ "| AverageEpRet | -6.3e+03 |\n",
+ "| StdEpRet | 1.14e+03 |\n",
+ "| MaxEpRet | -3.87e+03 |\n",
+ "| MinEpRet | -8.1e+03 |\n",
+ "| AverageTestEpRet | -4.27e+03 |\n",
+ "| StdTestEpRet | 1.52e+03 |\n",
+ "| MaxTestEpRet | -1.82e+03 |\n",
+ "| MinTestEpRet | -6.62e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+04 |\n",
+ "| AverageQ1Vals | -552 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | -15.7 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -552 |\n",
+ "| StdQ2Vals | 172 |\n",
+ "| MaxQ2Vals | 17.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 508 |\n",
+ "| LossQ | 4.63e+03 |\n",
+ "| Time | 116 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 6 |\n",
+ "| AverageEpRet | -6.76e+03 |\n",
+ "| StdEpRet | 1.35e+03 |\n",
+ "| MaxEpRet | -4.61e+03 |\n",
+ "| MinEpRet | -1.13e+04 |\n",
+ "| AverageTestEpRet | -3.67e+03 |\n",
+ "| StdTestEpRet | 994 |\n",
+ "| MaxTestEpRet | -2.24e+03 |\n",
+ "| MinTestEpRet | -5.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+04 |\n",
+ "| AverageQ1Vals | -574 |\n",
+ "| StdQ1Vals | 186 |\n",
+ "| MaxQ1Vals | -39.4 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -574 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | -27.9 |\n",
+ "| MinQ2Vals | -1.29e+03 |\n",
+ "| LossPi | 527 |\n",
+ "| LossQ | 5.18e+03 |\n",
+ "| Time | 139 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 7 |\n",
+ "| AverageEpRet | -7.02e+03 |\n",
+ "| StdEpRet | 1.79e+03 |\n",
+ "| MaxEpRet | -3.34e+03 |\n",
+ "| MinEpRet | -9.5e+03 |\n",
+ "| AverageTestEpRet | -3.05e+03 |\n",
+ "| StdTestEpRet | 1.56e+03 |\n",
+ "| MaxTestEpRet | -1.34e+03 |\n",
+ "| MinTestEpRet | -6.7e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+04 |\n",
+ "| AverageQ1Vals | -582 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | -68.9 |\n",
+ "| MinQ1Vals | -1.38e+03 |\n",
+ "| AverageQ2Vals | -582 |\n",
+ "| StdQ2Vals | 195 |\n",
+ "| MaxQ2Vals | -43.9 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 534 |\n",
+ "| LossQ | 5.26e+03 |\n",
+ "| Time | 163 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 8 |\n",
+ "| AverageEpRet | -6.62e+03 |\n",
+ "| StdEpRet | 1.42e+03 |\n",
+ "| MaxEpRet | -4.04e+03 |\n",
+ "| MinEpRet | -9.02e+03 |\n",
+ "| AverageTestEpRet | -1.54e+03 |\n",
+ "| StdTestEpRet | 771 |\n",
+ "| MaxTestEpRet | -595 |\n",
+ "| MinTestEpRet | -3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+04 |\n",
+ "| AverageQ1Vals | -573 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | -4.68 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -573 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 22.9 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 522 |\n",
+ "| LossQ | 5.51e+03 |\n",
+ "| Time | 187 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 9 |\n",
+ "| AverageEpRet | -6.7e+03 |\n",
+ "| StdEpRet | 1.58e+03 |\n",
+ "| MaxEpRet | -4.21e+03 |\n",
+ "| MinEpRet | -9.46e+03 |\n",
+ "| AverageTestEpRet | -842 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -411 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+04 |\n",
+ "| AverageQ1Vals | -546 |\n",
+ "| StdQ1Vals | 223 |\n",
+ "| MaxQ1Vals | 71.2 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -546 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 60.5 |\n",
+ "| MinQ2Vals | -1.41e+03 |\n",
+ "| LossPi | 490 |\n",
+ "| LossQ | 5.83e+03 |\n",
+ "| Time | 211 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 10 |\n",
+ "| AverageEpRet | -1.66e+03 |\n",
+ "| StdEpRet | 1.99e+03 |\n",
+ "| MaxEpRet | -153 |\n",
+ "| MinEpRet | -8.64e+03 |\n",
+ "| AverageTestEpRet | -872 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -487 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+04 |\n",
+ "| AverageQ1Vals | -488 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 151 |\n",
+ "| MinQ1Vals | -1.43e+03 |\n",
+ "| AverageQ2Vals | -488 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 127 |\n",
+ "| MinQ2Vals | -1.4e+03 |\n",
+ "| LossPi | 432 |\n",
+ "| LossQ | 5.85e+03 |\n",
+ "| Time | 239 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 11 |\n",
+ "| AverageEpRet | -947 |\n",
+ "| StdEpRet | 493 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.89e+03 |\n",
+ "| AverageTestEpRet | -1.09e+03 |\n",
+ "| StdTestEpRet | 661 |\n",
+ "| MaxTestEpRet | -271 |\n",
+ "| MinTestEpRet | -2.78e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+04 |\n",
+ "| AverageQ1Vals | -425 |\n",
+ "| StdQ1Vals | 258 |\n",
+ "| MaxQ1Vals | 141 |\n",
+ "| MinQ1Vals | -1.4e+03 |\n",
+ "| AverageQ2Vals | -425 |\n",
+ "| StdQ2Vals | 258 |\n",
+ "| MaxQ2Vals | 137 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 376 |\n",
+ "| LossQ | 4.71e+03 |\n",
+ "| Time | 263 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 12 |\n",
+ "| AverageEpRet | -888 |\n",
+ "| StdEpRet | 472 |\n",
+ "| MaxEpRet | -325 |\n",
+ "| MinEpRet | -2.33e+03 |\n",
+ "| AverageTestEpRet | -602 |\n",
+ "| StdTestEpRet | 199 |\n",
+ "| MaxTestEpRet | -286 |\n",
+ "| MinTestEpRet | -910 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+04 |\n",
+ "| AverageQ1Vals | -384 |\n",
+ "| StdQ1Vals | 264 |\n",
+ "| MaxQ1Vals | 161 |\n",
+ "| MinQ1Vals | -1.41e+03 |\n",
+ "| AverageQ2Vals | -384 |\n",
+ "| StdQ2Vals | 264 |\n",
+ "| MaxQ2Vals | 142 |\n",
+ "| MinQ2Vals | -1.39e+03 |\n",
+ "| LossPi | 341 |\n",
+ "| LossQ | 3.72e+03 |\n",
+ "| Time | 288 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 13 |\n",
+ "| AverageEpRet | -797 |\n",
+ "| StdEpRet | 536 |\n",
+ "| MaxEpRet | -48.6 |\n",
+ "| MinEpRet | -2.46e+03 |\n",
+ "| AverageTestEpRet | -857 |\n",
+ "| StdTestEpRet | 354 |\n",
+ "| MaxTestEpRet | -467 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.86e+04 |\n",
+ "| AverageQ1Vals | -354 |\n",
+ "| StdQ1Vals | 270 |\n",
+ "| MaxQ1Vals | 169 |\n",
+ "| MinQ1Vals | -1.36e+03 |\n",
+ "| AverageQ2Vals | -354 |\n",
+ "| StdQ2Vals | 270 |\n",
+ "| MaxQ2Vals | 166 |\n",
+ "| MinQ2Vals | -1.35e+03 |\n",
+ "| LossPi | 314 |\n",
+ "| LossQ | 3.07e+03 |\n",
+ "| Time | 312 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 14 |\n",
+ "| AverageEpRet | -926 |\n",
+ "| StdEpRet | 418 |\n",
+ "| MaxEpRet | -333 |\n",
+ "| MinEpRet | -1.67e+03 |\n",
+ "| AverageTestEpRet | -902 |\n",
+ "| StdTestEpRet | 481 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -2.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.08e+04 |\n",
+ "| AverageQ1Vals | -326 |\n",
+ "| StdQ1Vals | 274 |\n",
+ "| MaxQ1Vals | 192 |\n",
+ "| MinQ1Vals | -1.34e+03 |\n",
+ "| AverageQ2Vals | -326 |\n",
+ "| StdQ2Vals | 274 |\n",
+ "| MaxQ2Vals | 191 |\n",
+ "| MinQ2Vals | -1.34e+03 |\n",
+ "| LossPi | 288 |\n",
+ "| LossQ | 2.66e+03 |\n",
+ "| Time | 337 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 15 |\n",
+ "| AverageEpRet | -738 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -267 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -1.45e+03 |\n",
+ "| StdTestEpRet | 1.42e+03 |\n",
+ "| MaxTestEpRet | -185 |\n",
+ "| MinTestEpRet | -4.34e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.3e+04 |\n",
+ "| AverageQ1Vals | -297 |\n",
+ "| StdQ1Vals | 279 |\n",
+ "| MaxQ1Vals | 220 |\n",
+ "| MinQ1Vals | -1.31e+03 |\n",
+ "| AverageQ2Vals | -297 |\n",
+ "| StdQ2Vals | 279 |\n",
+ "| MaxQ2Vals | 221 |\n",
+ "| MinQ2Vals | -1.31e+03 |\n",
+ "| LossPi | 261 |\n",
+ "| LossQ | 2.37e+03 |\n",
+ "| Time | 362 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 16 |\n",
+ "| AverageEpRet | -1.37e+03 |\n",
+ "| StdEpRet | 1.85e+03 |\n",
+ "| MaxEpRet | -223 |\n",
+ "| MinEpRet | -8.63e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 159 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -913 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.52e+04 |\n",
+ "| AverageQ1Vals | -267 |\n",
+ "| StdQ1Vals | 277 |\n",
+ "| MaxQ1Vals | 279 |\n",
+ "| MinQ1Vals | -1.3e+03 |\n",
+ "| AverageQ2Vals | -267 |\n",
+ "| StdQ2Vals | 277 |\n",
+ "| MaxQ2Vals | 269 |\n",
+ "| MinQ2Vals | -1.27e+03 |\n",
+ "| LossPi | 231 |\n",
+ "| LossQ | 2.21e+03 |\n",
+ "| Time | 386 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 17 |\n",
+ "| AverageEpRet | -754 |\n",
+ "| StdEpRet | 595 |\n",
+ "| MaxEpRet | -202 |\n",
+ "| MinEpRet | -2.5e+03 |\n",
+ "| AverageTestEpRet | -636 |\n",
+ "| StdTestEpRet | 389 |\n",
+ "| MaxTestEpRet | -147 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.74e+04 |\n",
+ "| AverageQ1Vals | -245 |\n",
+ "| StdQ1Vals | 273 |\n",
+ "| MaxQ1Vals | 278 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -245 |\n",
+ "| StdQ2Vals | 273 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 211 |\n",
+ "| LossQ | 2.03e+03 |\n",
+ "| Time | 411 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 18 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 339 |\n",
+ "| MaxEpRet | -241 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -713 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -419 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 3.96e+04 |\n",
+ "| AverageQ1Vals | -226 |\n",
+ "| StdQ1Vals | 268 |\n",
+ "| MaxQ1Vals | 275 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -226 |\n",
+ "| StdQ2Vals | 268 |\n",
+ "| MaxQ2Vals | 265 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 194 |\n",
+ "| LossQ | 1.83e+03 |\n",
+ "| Time | 434 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 19 |\n",
+ "| AverageEpRet | -745 |\n",
+ "| StdEpRet | 537 |\n",
+ "| MaxEpRet | -109 |\n",
+ "| MinEpRet | -2.14e+03 |\n",
+ "| AverageTestEpRet | -963 |\n",
+ "| StdTestEpRet | 641 |\n",
+ "| MaxTestEpRet | -159 |\n",
+ "| MinTestEpRet | -2.65e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.18e+04 |\n",
+ "| AverageQ1Vals | -214 |\n",
+ "| StdQ1Vals | 263 |\n",
+ "| MaxQ1Vals | 273 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -214 |\n",
+ "| StdQ2Vals | 263 |\n",
+ "| MaxQ2Vals | 266 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 183 |\n",
+ "| LossQ | 1.67e+03 |\n",
+ "| Time | 458 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 20 |\n",
+ "| AverageEpRet | -711 |\n",
+ "| StdEpRet | 511 |\n",
+ "| MaxEpRet | -18.3 |\n",
+ "| MinEpRet | -1.83e+03 |\n",
+ "| AverageTestEpRet | -803 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -126 |\n",
+ "| MinTestEpRet | -1.53e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.4e+04 |\n",
+ "| AverageQ1Vals | -198 |\n",
+ "| StdQ1Vals | 260 |\n",
+ "| MaxQ1Vals | 276 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -198 |\n",
+ "| StdQ2Vals | 260 |\n",
+ "| MaxQ2Vals | 268 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 169 |\n",
+ "| LossQ | 1.57e+03 |\n",
+ "| Time | 483 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 21 |\n",
+ "| AverageEpRet | -664 |\n",
+ "| StdEpRet | 403 |\n",
+ "| MaxEpRet | -176 |\n",
+ "| MinEpRet | -1.87e+03 |\n",
+ "| AverageTestEpRet | -577 |\n",
+ "| StdTestEpRet | 343 |\n",
+ "| MaxTestEpRet | -133 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.62e+04 |\n",
+ "| AverageQ1Vals | -187 |\n",
+ "| StdQ1Vals | 254 |\n",
+ "| MaxQ1Vals | 269 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -187 |\n",
+ "| StdQ2Vals | 254 |\n",
+ "| MaxQ2Vals | 262 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 159 |\n",
+ "| LossQ | 1.48e+03 |\n",
+ "| Time | 507 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 22 |\n",
+ "| AverageEpRet | -662 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -261 |\n",
+ "| MinEpRet | -1.64e+03 |\n",
+ "| AverageTestEpRet | -780 |\n",
+ "| StdTestEpRet | 458 |\n",
+ "| MaxTestEpRet | -39.9 |\n",
+ "| MinTestEpRet | -1.77e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 4.84e+04 |\n",
+ "| AverageQ1Vals | -178 |\n",
+ "| StdQ1Vals | 249 |\n",
+ "| MaxQ1Vals | 260 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -178 |\n",
+ "| StdQ2Vals | 249 |\n",
+ "| MaxQ2Vals | 258 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 151 |\n",
+ "| LossQ | 1.35e+03 |\n",
+ "| Time | 531 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 23 |\n",
+ "| AverageEpRet | -524 |\n",
+ "| StdEpRet | 312 |\n",
+ "| MaxEpRet | -85.9 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -768 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -329 |\n",
+ "| MinTestEpRet | -1.3e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.06e+04 |\n",
+ "| AverageQ1Vals | -171 |\n",
+ "| StdQ1Vals | 243 |\n",
+ "| MaxQ1Vals | 239 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -170 |\n",
+ "| StdQ2Vals | 243 |\n",
+ "| MaxQ2Vals | 246 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 145 |\n",
+ "| LossQ | 1.29e+03 |\n",
+ "| Time | 555 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 24 |\n",
+ "| AverageEpRet | -714 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -181 |\n",
+ "| MinEpRet | -1.29e+03 |\n",
+ "| AverageTestEpRet | -433 |\n",
+ "| StdTestEpRet | 184 |\n",
+ "| MaxTestEpRet | -178 |\n",
+ "| MinTestEpRet | -786 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.28e+04 |\n",
+ "| AverageQ1Vals | -164 |\n",
+ "| StdQ1Vals | 238 |\n",
+ "| MaxQ1Vals | 227 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -164 |\n",
+ "| StdQ2Vals | 238 |\n",
+ "| MaxQ2Vals | 229 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 139 |\n",
+ "| LossQ | 1.22e+03 |\n",
+ "| Time | 579 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 25 |\n",
+ "| AverageEpRet | -709 |\n",
+ "| StdEpRet | 425 |\n",
+ "| MaxEpRet | -88.8 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -808 |\n",
+ "| StdTestEpRet | 476 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.81e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.5e+04 |\n",
+ "| AverageQ1Vals | -159 |\n",
+ "| StdQ1Vals | 233 |\n",
+ "| MaxQ1Vals | 209 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -159 |\n",
+ "| StdQ2Vals | 233 |\n",
+ "| MaxQ2Vals | 209 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.15e+03 |\n",
+ "| Time | 603 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 26 |\n",
+ "| AverageEpRet | -813 |\n",
+ "| StdEpRet | 433 |\n",
+ "| MaxEpRet | -247 |\n",
+ "| MinEpRet | -1.81e+03 |\n",
+ "| AverageTestEpRet | -702 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.72e+04 |\n",
+ "| AverageQ1Vals | -158 |\n",
+ "| StdQ1Vals | 231 |\n",
+ "| MaxQ1Vals | 194 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -158 |\n",
+ "| StdQ2Vals | 231 |\n",
+ "| MaxQ2Vals | 198 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 136 |\n",
+ "| LossQ | 1.14e+03 |\n",
+ "| Time | 628 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 27 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 282 |\n",
+ "| MaxEpRet | -336 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -934 |\n",
+ "| StdTestEpRet | 626 |\n",
+ "| MaxTestEpRet | -317 |\n",
+ "| MinTestEpRet | -2.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 5.94e+04 |\n",
+ "| AverageQ1Vals | -157 |\n",
+ "| StdQ1Vals | 229 |\n",
+ "| MaxQ1Vals | 190 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -157 |\n",
+ "| StdQ2Vals | 229 |\n",
+ "| MaxQ2Vals | 192 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 135 |\n",
+ "| LossQ | 1.03e+03 |\n",
+ "| Time | 653 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 28 |\n",
+ "| AverageEpRet | -965 |\n",
+ "| StdEpRet | 522 |\n",
+ "| MaxEpRet | -172 |\n",
+ "| MinEpRet | -2.3e+03 |\n",
+ "| AverageTestEpRet | -599 |\n",
+ "| StdTestEpRet | 398 |\n",
+ "| MaxTestEpRet | -163 |\n",
+ "| MinTestEpRet | -1.32e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.16e+04 |\n",
+ "| AverageQ1Vals | -154 |\n",
+ "| StdQ1Vals | 226 |\n",
+ "| MaxQ1Vals | 176 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -154 |\n",
+ "| StdQ2Vals | 226 |\n",
+ "| MaxQ2Vals | 188 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 973 |\n",
+ "| Time | 677 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 29 |\n",
+ "| AverageEpRet | -766 |\n",
+ "| StdEpRet | 367 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.69e+03 |\n",
+ "| AverageTestEpRet | -659 |\n",
+ "| StdTestEpRet | 229 |\n",
+ "| MaxTestEpRet | -303 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.38e+04 |\n",
+ "| AverageQ1Vals | -153 |\n",
+ "| StdQ1Vals | 225 |\n",
+ "| MaxQ1Vals | 173 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -153 |\n",
+ "| StdQ2Vals | 225 |\n",
+ "| MaxQ2Vals | 176 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 133 |\n",
+ "| LossQ | 961 |\n",
+ "| Time | 702 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 30 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -388 |\n",
+ "| MinEpRet | -1.46e+03 |\n",
+ "| AverageTestEpRet | -686 |\n",
+ "| StdTestEpRet | 188 |\n",
+ "| MaxTestEpRet | -312 |\n",
+ "| MinTestEpRet | -977 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.6e+04 |\n",
+ "| AverageQ1Vals | -151 |\n",
+ "| StdQ1Vals | 222 |\n",
+ "| MaxQ1Vals | 162 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -151 |\n",
+ "| StdQ2Vals | 222 |\n",
+ "| MaxQ2Vals | 159 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 131 |\n",
+ "| LossQ | 913 |\n",
+ "| Time | 727 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 31 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 442 |\n",
+ "| MaxEpRet | -163 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -678 |\n",
+ "| StdTestEpRet | 371 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 6.82e+04 |\n",
+ "| AverageQ1Vals | -149 |\n",
+ "| StdQ1Vals | 219 |\n",
+ "| MaxQ1Vals | 149 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -149 |\n",
+ "| StdQ2Vals | 219 |\n",
+ "| MaxQ2Vals | 153 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 855 |\n",
+ "| Time | 753 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 32 |\n",
+ "| AverageEpRet | -760 |\n",
+ "| StdEpRet | 313 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -1.49e+03 |\n",
+ "| AverageTestEpRet | -759 |\n",
+ "| StdTestEpRet | 430 |\n",
+ "| MaxTestEpRet | -256 |\n",
+ "| MinTestEpRet | -1.64e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.04e+04 |\n",
+ "| AverageQ1Vals | -150 |\n",
+ "| StdQ1Vals | 217 |\n",
+ "| MaxQ1Vals | 146 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -150 |\n",
+ "| StdQ2Vals | 217 |\n",
+ "| MaxQ2Vals | 145 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 130 |\n",
+ "| LossQ | 835 |\n",
+ "| Time | 775 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 33 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 395 |\n",
+ "| MaxEpRet | -57.1 |\n",
+ "| MinEpRet | -1.4e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 368 |\n",
+ "| MaxTestEpRet | -120 |\n",
+ "| MinTestEpRet | -1.52e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.26e+04 |\n",
+ "| AverageQ1Vals | -147 |\n",
+ "| StdQ1Vals | 213 |\n",
+ "| MaxQ1Vals | 130 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -147 |\n",
+ "| StdQ2Vals | 213 |\n",
+ "| MaxQ2Vals | 125 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 129 |\n",
+ "| LossQ | 800 |\n",
+ "| Time | 796 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 34 |\n",
+ "| AverageEpRet | -732 |\n",
+ "| StdEpRet | 361 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.6e+03 |\n",
+ "| AverageTestEpRet | -737 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -278 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.48e+04 |\n",
+ "| AverageQ1Vals | -144 |\n",
+ "| StdQ1Vals | 211 |\n",
+ "| MaxQ1Vals | 133 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -144 |\n",
+ "| StdQ2Vals | 211 |\n",
+ "| MaxQ2Vals | 121 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 126 |\n",
+ "| LossQ | 784 |\n",
+ "| Time | 820 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 35 |\n",
+ "| AverageEpRet | -682 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -159 |\n",
+ "| MinEpRet | -1.58e+03 |\n",
+ "| AverageTestEpRet | -766 |\n",
+ "| StdTestEpRet | 358 |\n",
+ "| MaxTestEpRet | -85.9 |\n",
+ "| MinTestEpRet | -1.35e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.7e+04 |\n",
+ "| AverageQ1Vals | -143 |\n",
+ "| StdQ1Vals | 209 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -143 |\n",
+ "| StdQ2Vals | 209 |\n",
+ "| MaxQ2Vals | 107 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 730 |\n",
+ "| Time | 846 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 36 |\n",
+ "| AverageEpRet | -848 |\n",
+ "| StdEpRet | 733 |\n",
+ "| MaxEpRet | -216 |\n",
+ "| MinEpRet | -3.32e+03 |\n",
+ "| AverageTestEpRet | -765 |\n",
+ "| StdTestEpRet | 340 |\n",
+ "| MaxTestEpRet | -191 |\n",
+ "| MinTestEpRet | -1.48e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 7.92e+04 |\n",
+ "| AverageQ1Vals | -141 |\n",
+ "| StdQ1Vals | 207 |\n",
+ "| MaxQ1Vals | 115 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -141 |\n",
+ "| StdQ2Vals | 207 |\n",
+ "| MaxQ2Vals | 108 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 125 |\n",
+ "| LossQ | 703 |\n",
+ "| Time | 873 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 37 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 379 |\n",
+ "| MaxEpRet | -11.3 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -646 |\n",
+ "| StdTestEpRet | 399 |\n",
+ "| MaxTestEpRet | -102 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.14e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 204 |\n",
+ "| MaxQ1Vals | 103 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 204 |\n",
+ "| MaxQ2Vals | 96.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 667 |\n",
+ "| Time | 900 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 38 |\n",
+ "| AverageEpRet | -634 |\n",
+ "| StdEpRet | 304 |\n",
+ "| MaxEpRet | -44.1 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -716 |\n",
+ "| StdTestEpRet | 356 |\n",
+ "| MaxTestEpRet | -263 |\n",
+ "| MinTestEpRet | -1.49e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.36e+04 |\n",
+ "| AverageQ1Vals | -140 |\n",
+ "| StdQ1Vals | 203 |\n",
+ "| MaxQ1Vals | 91.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -140 |\n",
+ "| StdQ2Vals | 203 |\n",
+ "| MaxQ2Vals | 86 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 664 |\n",
+ "| Time | 929 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 39 |\n",
+ "| AverageEpRet | -630 |\n",
+ "| StdEpRet | 375 |\n",
+ "| MaxEpRet | -127 |\n",
+ "| MinEpRet | -1.59e+03 |\n",
+ "| AverageTestEpRet | -591 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -204 |\n",
+ "| MinTestEpRet | -946 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.58e+04 |\n",
+ "| AverageQ1Vals | -139 |\n",
+ "| StdQ1Vals | 201 |\n",
+ "| MaxQ1Vals | 86.8 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -139 |\n",
+ "| StdQ2Vals | 201 |\n",
+ "| MaxQ2Vals | 77.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 124 |\n",
+ "| LossQ | 649 |\n",
+ "| Time | 960 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 40 |\n",
+ "| AverageEpRet | -506 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -67.2 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -621 |\n",
+ "| StdTestEpRet | 351 |\n",
+ "| MaxTestEpRet | -171 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 8.8e+04 |\n",
+ "| AverageQ1Vals | -137 |\n",
+ "| StdQ1Vals | 199 |\n",
+ "| MaxQ1Vals | 77.5 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -137 |\n",
+ "| StdQ2Vals | 199 |\n",
+ "| MaxQ2Vals | 66.2 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 122 |\n",
+ "| LossQ | 629 |\n",
+ "| Time | 991 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 41 |\n",
+ "| AverageEpRet | -676 |\n",
+ "| StdEpRet | 301 |\n",
+ "| MaxEpRet | -182 |\n",
+ "| MinEpRet | -1.31e+03 |\n",
+ "| AverageTestEpRet | -784 |\n",
+ "| StdTestEpRet | 309 |\n",
+ "| MaxTestEpRet | -408 |\n",
+ "| MinTestEpRet | -1.4e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.02e+04 |\n",
+ "| AverageQ1Vals | -136 |\n",
+ "| StdQ1Vals | 197 |\n",
+ "| MaxQ1Vals | 74.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -136 |\n",
+ "| StdQ2Vals | 197 |\n",
+ "| MaxQ2Vals | 74.3 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 121 |\n",
+ "| LossQ | 587 |\n",
+ "| Time | 1.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 42 |\n",
+ "| AverageEpRet | -623 |\n",
+ "| StdEpRet | 295 |\n",
+ "| MaxEpRet | -69.3 |\n",
+ "| MinEpRet | -1.19e+03 |\n",
+ "| AverageTestEpRet | -806 |\n",
+ "| StdTestEpRet | 386 |\n",
+ "| MaxTestEpRet | -285 |\n",
+ "| MinTestEpRet | -1.63e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.24e+04 |\n",
+ "| AverageQ1Vals | -134 |\n",
+ "| StdQ1Vals | 196 |\n",
+ "| MaxQ1Vals | 59.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -134 |\n",
+ "| StdQ2Vals | 196 |\n",
+ "| MaxQ2Vals | 67.8 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 120 |\n",
+ "| LossQ | 584 |\n",
+ "| Time | 1.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 43 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 255 |\n",
+ "| MaxEpRet | -79.6 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -620 |\n",
+ "| StdTestEpRet | 300 |\n",
+ "| MaxTestEpRet | -160 |\n",
+ "| MinTestEpRet | -971 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.46e+04 |\n",
+ "| AverageQ1Vals | -133 |\n",
+ "| StdQ1Vals | 194 |\n",
+ "| MaxQ1Vals | 63.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -133 |\n",
+ "| StdQ2Vals | 194 |\n",
+ "| MaxQ2Vals | 68.2 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 561 |\n",
+ "| Time | 1.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 44 |\n",
+ "| AverageEpRet | -583 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -63.3 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -507 |\n",
+ "| StdTestEpRet | 296 |\n",
+ "| MaxTestEpRet | -107 |\n",
+ "| MinTestEpRet | -996 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.68e+04 |\n",
+ "| AverageQ1Vals | -132 |\n",
+ "| StdQ1Vals | 191 |\n",
+ "| MaxQ1Vals | 49.2 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -132 |\n",
+ "| StdQ2Vals | 191 |\n",
+ "| MaxQ2Vals | 49.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 118 |\n",
+ "| LossQ | 532 |\n",
+ "| Time | 1.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 45 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 309 |\n",
+ "| MaxEpRet | -147 |\n",
+ "| MinEpRet | -1.23e+03 |\n",
+ "| AverageTestEpRet | -727 |\n",
+ "| StdTestEpRet | 348 |\n",
+ "| MaxTestEpRet | -203 |\n",
+ "| MinTestEpRet | -1.43e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 9.9e+04 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 50.6 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 47.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 116 |\n",
+ "| LossQ | 517 |\n",
+ "| Time | 1.14e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 46 |\n",
+ "| AverageEpRet | -649 |\n",
+ "| StdEpRet | 325 |\n",
+ "| MaxEpRet | -151 |\n",
+ "| MinEpRet | -1.5e+03 |\n",
+ "| AverageTestEpRet | -647 |\n",
+ "| StdTestEpRet | 317 |\n",
+ "| MaxTestEpRet | -219 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.01e+05 |\n",
+ "| AverageQ1Vals | -129 |\n",
+ "| StdQ1Vals | 187 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.1e+03 |\n",
+ "| AverageQ2Vals | -129 |\n",
+ "| StdQ2Vals | 187 |\n",
+ "| MaxQ2Vals | 90.9 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 115 |\n",
+ "| LossQ | 514 |\n",
+ "| Time | 1.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 47 |\n",
+ "| AverageEpRet | -444 |\n",
+ "| StdEpRet | 183 |\n",
+ "| MaxEpRet | -183 |\n",
+ "| MinEpRet | -968 |\n",
+ "| AverageTestEpRet | -669 |\n",
+ "| StdTestEpRet | 245 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.12e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.03e+05 |\n",
+ "| AverageQ1Vals | -127 |\n",
+ "| StdQ1Vals | 185 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -127 |\n",
+ "| StdQ2Vals | 185 |\n",
+ "| MaxQ2Vals | 55.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 113 |\n",
+ "| LossQ | 489 |\n",
+ "| Time | 1.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 48 |\n",
+ "| AverageEpRet | -673 |\n",
+ "| StdEpRet | 332 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.41e+03 |\n",
+ "| AverageTestEpRet | -534 |\n",
+ "| StdTestEpRet | 252 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.06e+05 |\n",
+ "| AverageQ1Vals | -125 |\n",
+ "| StdQ1Vals | 182 |\n",
+ "| MaxQ1Vals | 96.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -125 |\n",
+ "| StdQ2Vals | 182 |\n",
+ "| MaxQ2Vals | 93.5 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 473 |\n",
+ "| Time | 1.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 49 |\n",
+ "| AverageEpRet | -475 |\n",
+ "| StdEpRet | 243 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -572 |\n",
+ "| StdTestEpRet | 339 |\n",
+ "| MaxTestEpRet | -155 |\n",
+ "| MinTestEpRet | -1.25e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.08e+05 |\n",
+ "| AverageQ1Vals | -124 |\n",
+ "| StdQ1Vals | 181 |\n",
+ "| MaxQ1Vals | 66.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -124 |\n",
+ "| StdQ2Vals | 181 |\n",
+ "| MaxQ2Vals | 60.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 112 |\n",
+ "| LossQ | 466 |\n",
+ "| Time | 1.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 50 |\n",
+ "| AverageEpRet | -581 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -121 |\n",
+ "| MinEpRet | -1.34e+03 |\n",
+ "| AverageTestEpRet | -687 |\n",
+ "| StdTestEpRet | 246 |\n",
+ "| MaxTestEpRet | -246 |\n",
+ "| MinTestEpRet | -1.07e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.1e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 50.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 439 |\n",
+ "| Time | 1.31e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 51 |\n",
+ "| AverageEpRet | -599 |\n",
+ "| StdEpRet | 287 |\n",
+ "| MaxEpRet | -232 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 204 |\n",
+ "| MaxTestEpRet | -216 |\n",
+ "| MinTestEpRet | -856 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.12e+05 |\n",
+ "| AverageQ1Vals | -122 |\n",
+ "| StdQ1Vals | 179 |\n",
+ "| MaxQ1Vals | 78.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -122 |\n",
+ "| StdQ2Vals | 179 |\n",
+ "| MaxQ2Vals | 76.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 110 |\n",
+ "| LossQ | 437 |\n",
+ "| Time | 1.34e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 52 |\n",
+ "| AverageEpRet | -636 |\n",
+ "| StdEpRet | 283 |\n",
+ "| MaxEpRet | -220 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -712 |\n",
+ "| StdTestEpRet | 185 |\n",
+ "| MaxTestEpRet | -457 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.14e+05 |\n",
+ "| AverageQ1Vals | -121 |\n",
+ "| StdQ1Vals | 178 |\n",
+ "| MaxQ1Vals | 58.8 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -121 |\n",
+ "| StdQ2Vals | 178 |\n",
+ "| MaxQ2Vals | 54.7 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 109 |\n",
+ "| LossQ | 431 |\n",
+ "| Time | 1.37e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 53 |\n",
+ "| AverageEpRet | -591 |\n",
+ "| StdEpRet | 351 |\n",
+ "| MaxEpRet | -132 |\n",
+ "| MinEpRet | -1.43e+03 |\n",
+ "| AverageTestEpRet | -551 |\n",
+ "| StdTestEpRet | 206 |\n",
+ "| MaxTestEpRet | -333 |\n",
+ "| MinTestEpRet | -975 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.17e+05 |\n",
+ "| AverageQ1Vals | -119 |\n",
+ "| StdQ1Vals | 176 |\n",
+ "| MaxQ1Vals | 54.4 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -119 |\n",
+ "| StdQ2Vals | 176 |\n",
+ "| MaxQ2Vals | 89.1 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 108 |\n",
+ "| LossQ | 418 |\n",
+ "| Time | 1.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 54 |\n",
+ "| AverageEpRet | -426 |\n",
+ "| StdEpRet | 230 |\n",
+ "| MaxEpRet | -90.2 |\n",
+ "| MinEpRet | -906 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 197 |\n",
+ "| MaxTestEpRet | -124 |\n",
+ "| MinTestEpRet | -708 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.19e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 175 |\n",
+ "| MaxQ1Vals | 47.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 175 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 401 |\n",
+ "| Time | 1.43e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 55 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -939 |\n",
+ "| AverageTestEpRet | -615 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -400 |\n",
+ "| MinTestEpRet | -952 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.21e+05 |\n",
+ "| AverageQ1Vals | -118 |\n",
+ "| StdQ1Vals | 174 |\n",
+ "| MaxQ1Vals | 54.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -118 |\n",
+ "| StdQ2Vals | 174 |\n",
+ "| MaxQ2Vals | 68.6 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 106 |\n",
+ "| LossQ | 392 |\n",
+ "| Time | 1.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 56 |\n",
+ "| AverageEpRet | -552 |\n",
+ "| StdEpRet | 234 |\n",
+ "| MaxEpRet | -228 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -400 |\n",
+ "| StdTestEpRet | 293 |\n",
+ "| MaxTestEpRet | -66.3 |\n",
+ "| MinTestEpRet | -1.1e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.23e+05 |\n",
+ "| AverageQ1Vals | -117 |\n",
+ "| StdQ1Vals | 173 |\n",
+ "| MaxQ1Vals | 66.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -117 |\n",
+ "| StdQ2Vals | 173 |\n",
+ "| MaxQ2Vals | 69.5 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 105 |\n",
+ "| LossQ | 400 |\n",
+ "| Time | 1.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 57 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 318 |\n",
+ "| MaxEpRet | -33.7 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -480 |\n",
+ "| StdTestEpRet | 210 |\n",
+ "| MaxTestEpRet | -197 |\n",
+ "| MinTestEpRet | -802 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.25e+05 |\n",
+ "| AverageQ1Vals | -114 |\n",
+ "| StdQ1Vals | 171 |\n",
+ "| MaxQ1Vals | 68.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -114 |\n",
+ "| StdQ2Vals | 171 |\n",
+ "| MaxQ2Vals | 68.8 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 103 |\n",
+ "| LossQ | 380 |\n",
+ "| Time | 1.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 58 |\n",
+ "| AverageEpRet | -590 |\n",
+ "| StdEpRet | 261 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 234 |\n",
+ "| MaxTestEpRet | -206 |\n",
+ "| MinTestEpRet | -935 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.28e+05 |\n",
+ "| AverageQ1Vals | -113 |\n",
+ "| StdQ1Vals | 170 |\n",
+ "| MaxQ1Vals | 72.9 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -113 |\n",
+ "| StdQ2Vals | 170 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 368 |\n",
+ "| Time | 1.56e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 59 |\n",
+ "| AverageEpRet | -588 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -1.05e+03 |\n",
+ "| AverageTestEpRet | -467 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -763 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.3e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 169 |\n",
+ "| MaxQ1Vals | 64.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 169 |\n",
+ "| MaxQ2Vals | 93.3 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 102 |\n",
+ "| LossQ | 367 |\n",
+ "| Time | 1.6e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 60 |\n",
+ "| AverageEpRet | -495 |\n",
+ "| StdEpRet | 358 |\n",
+ "| MaxEpRet | -44.8 |\n",
+ "| MinEpRet | -1.71e+03 |\n",
+ "| AverageTestEpRet | -654 |\n",
+ "| StdTestEpRet | 261 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.05e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.32e+05 |\n",
+ "| AverageQ1Vals | -112 |\n",
+ "| StdQ1Vals | 168 |\n",
+ "| MaxQ1Vals | 68.9 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -112 |\n",
+ "| StdQ2Vals | 168 |\n",
+ "| MaxQ2Vals | 73.7 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 101 |\n",
+ "| LossQ | 363 |\n",
+ "| Time | 1.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 61 |\n",
+ "| AverageEpRet | -690 |\n",
+ "| StdEpRet | 376 |\n",
+ "| MaxEpRet | -74.9 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -574 |\n",
+ "| StdTestEpRet | 410 |\n",
+ "| MaxTestEpRet | -144 |\n",
+ "| MinTestEpRet | -1.57e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.34e+05 |\n",
+ "| AverageQ1Vals | -110 |\n",
+ "| StdQ1Vals | 166 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -110 |\n",
+ "| StdQ2Vals | 166 |\n",
+ "| MaxQ2Vals | 52.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 100 |\n",
+ "| LossQ | 349 |\n",
+ "| Time | 1.66e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 62 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -485 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -146 |\n",
+ "| MinTestEpRet | -886 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.36e+05 |\n",
+ "| AverageQ1Vals | -109 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 67.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -109 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 98.7 |\n",
+ "| LossQ | 358 |\n",
+ "| Time | 1.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 63 |\n",
+ "| AverageEpRet | -448 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -56.4 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -341 |\n",
+ "| StdTestEpRet | 181 |\n",
+ "| MaxTestEpRet | -63.6 |\n",
+ "| MinTestEpRet | -608 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.39e+05 |\n",
+ "| AverageQ1Vals | -108 |\n",
+ "| StdQ1Vals | 165 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -108 |\n",
+ "| StdQ2Vals | 165 |\n",
+ "| MaxQ2Vals | 52.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 97.8 |\n",
+ "| LossQ | 344 |\n",
+ "| Time | 1.73e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 64 |\n",
+ "| AverageEpRet | -525 |\n",
+ "| StdEpRet | 226 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -532 |\n",
+ "| StdTestEpRet | 182 |\n",
+ "| MaxTestEpRet | -302 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.41e+05 |\n",
+ "| AverageQ1Vals | -106 |\n",
+ "| StdQ1Vals | 164 |\n",
+ "| MaxQ1Vals | 50.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -106 |\n",
+ "| StdQ2Vals | 164 |\n",
+ "| MaxQ2Vals | 37.7 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 95.7 |\n",
+ "| LossQ | 340 |\n",
+ "| Time | 1.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 65 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -31.2 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -520 |\n",
+ "| StdTestEpRet | 359 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -1.51e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.43e+05 |\n",
+ "| AverageQ1Vals | -105 |\n",
+ "| StdQ1Vals | 162 |\n",
+ "| MaxQ1Vals | 58.3 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -105 |\n",
+ "| StdQ2Vals | 162 |\n",
+ "| MaxQ2Vals | 51.6 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 95.2 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 66 |\n",
+ "| AverageEpRet | -622 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -126 |\n",
+ "| MinEpRet | -1.33e+03 |\n",
+ "| AverageTestEpRet | -525 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.45e+05 |\n",
+ "| AverageQ1Vals | -104 |\n",
+ "| StdQ1Vals | 163 |\n",
+ "| MaxQ1Vals | 46.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -104 |\n",
+ "| StdQ2Vals | 163 |\n",
+ "| MaxQ2Vals | 73.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 94.4 |\n",
+ "| LossQ | 329 |\n",
+ "| Time | 1.83e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 67 |\n",
+ "| AverageEpRet | -483 |\n",
+ "| StdEpRet | 204 |\n",
+ "| MaxEpRet | -118 |\n",
+ "| MinEpRet | -836 |\n",
+ "| AverageTestEpRet | -530 |\n",
+ "| StdTestEpRet | 231 |\n",
+ "| MaxTestEpRet | -156 |\n",
+ "| MinTestEpRet | -994 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.47e+05 |\n",
+ "| AverageQ1Vals | -103 |\n",
+ "| StdQ1Vals | 161 |\n",
+ "| MaxQ1Vals | 56 |\n",
+ "| MinQ1Vals | -1.22e+03 |\n",
+ "| AverageQ2Vals | -103 |\n",
+ "| StdQ2Vals | 161 |\n",
+ "| MaxQ2Vals | 80 |\n",
+ "| MinQ2Vals | -1.24e+03 |\n",
+ "| LossPi | 93.2 |\n",
+ "| LossQ | 325 |\n",
+ "| Time | 1.86e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 68 |\n",
+ "| AverageEpRet | -443 |\n",
+ "| StdEpRet | 266 |\n",
+ "| MaxEpRet | -17.7 |\n",
+ "| MinEpRet | -926 |\n",
+ "| AverageTestEpRet | -498 |\n",
+ "| StdTestEpRet | 213 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -779 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.5e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 71.7 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 76.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.3 |\n",
+ "| LossQ | 311 |\n",
+ "| Time | 1.89e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 69 |\n",
+ "| AverageEpRet | -542 |\n",
+ "| StdEpRet | 422 |\n",
+ "| MaxEpRet | -74.6 |\n",
+ "| MinEpRet | -1.84e+03 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 205 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -905 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.52e+05 |\n",
+ "| AverageQ1Vals | -101 |\n",
+ "| StdQ1Vals | 160 |\n",
+ "| MaxQ1Vals | 57.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -101 |\n",
+ "| StdQ2Vals | 160 |\n",
+ "| MaxQ2Vals | 106 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 91.1 |\n",
+ "| LossQ | 315 |\n",
+ "| Time | 1.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 70 |\n",
+ "| AverageEpRet | -556 |\n",
+ "| StdEpRet | 188 |\n",
+ "| MaxEpRet | -166 |\n",
+ "| MinEpRet | -827 |\n",
+ "| AverageTestEpRet | -466 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -247 |\n",
+ "| MinTestEpRet | -782 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.54e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 55.3 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 104 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 89.6 |\n",
+ "| LossQ | 298 |\n",
+ "| Time | 1.96e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 71 |\n",
+ "| AverageEpRet | -508 |\n",
+ "| StdEpRet | 360 |\n",
+ "| MaxEpRet | -116 |\n",
+ "| MinEpRet | -1.65e+03 |\n",
+ "| AverageTestEpRet | -503 |\n",
+ "| StdTestEpRet | 290 |\n",
+ "| MaxTestEpRet | -98.4 |\n",
+ "| MinTestEpRet | -933 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.56e+05 |\n",
+ "| AverageQ1Vals | -98.9 |\n",
+ "| StdQ1Vals | 158 |\n",
+ "| MaxQ1Vals | 68.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -98.9 |\n",
+ "| StdQ2Vals | 158 |\n",
+ "| MaxQ2Vals | 62.4 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.9 |\n",
+ "| LossQ | 310 |\n",
+ "| Time | 1.98e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 72 |\n",
+ "| AverageEpRet | -565 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -154 |\n",
+ "| MinEpRet | -1.08e+03 |\n",
+ "| AverageTestEpRet | -595 |\n",
+ "| StdTestEpRet | 299 |\n",
+ "| MaxTestEpRet | -224 |\n",
+ "| MinTestEpRet | -1.16e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.58e+05 |\n",
+ "| AverageQ1Vals | -98.4 |\n",
+ "| StdQ1Vals | 157 |\n",
+ "| MaxQ1Vals | 40.1 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -98.4 |\n",
+ "| StdQ2Vals | 157 |\n",
+ "| MaxQ2Vals | 65.8 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 307 |\n",
+ "| Time | 2.01e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 73 |\n",
+ "| AverageEpRet | -531 |\n",
+ "| StdEpRet | 253 |\n",
+ "| MaxEpRet | -152 |\n",
+ "| MinEpRet | -1.18e+03 |\n",
+ "| AverageTestEpRet | -403 |\n",
+ "| StdTestEpRet | 239 |\n",
+ "| MaxTestEpRet | -66.2 |\n",
+ "| MinTestEpRet | -865 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.61e+05 |\n",
+ "| AverageQ1Vals | -98 |\n",
+ "| StdQ1Vals | 156 |\n",
+ "| MaxQ1Vals | 123 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -98 |\n",
+ "| StdQ2Vals | 156 |\n",
+ "| MaxQ2Vals | 61.2 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 89.2 |\n",
+ "| LossQ | 290 |\n",
+ "| Time | 2.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 74 |\n",
+ "| AverageEpRet | -608 |\n",
+ "| StdEpRet | 225 |\n",
+ "| MaxEpRet | -242 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -421 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -127 |\n",
+ "| MinTestEpRet | -972 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.63e+05 |\n",
+ "| AverageQ1Vals | -96.8 |\n",
+ "| StdQ1Vals | 154 |\n",
+ "| MaxQ1Vals | 84.5 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -96.8 |\n",
+ "| StdQ2Vals | 154 |\n",
+ "| MaxQ2Vals | 44 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 87.6 |\n",
+ "| LossQ | 280 |\n",
+ "| Time | 2.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 75 |\n",
+ "| AverageEpRet | -438 |\n",
+ "| StdEpRet | 207 |\n",
+ "| MaxEpRet | -103 |\n",
+ "| MinEpRet | -930 |\n",
+ "| AverageTestEpRet | -416 |\n",
+ "| StdTestEpRet | 241 |\n",
+ "| MaxTestEpRet | -135 |\n",
+ "| MinTestEpRet | -887 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.65e+05 |\n",
+ "| AverageQ1Vals | -96.7 |\n",
+ "| StdQ1Vals | 155 |\n",
+ "| MaxQ1Vals | 72.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -96.7 |\n",
+ "| StdQ2Vals | 155 |\n",
+ "| MaxQ2Vals | 75.7 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 88.1 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 76 |\n",
+ "| AverageEpRet | -639 |\n",
+ "| StdEpRet | 315 |\n",
+ "| MaxEpRet | -214 |\n",
+ "| MinEpRet | -1.51e+03 |\n",
+ "| AverageTestEpRet | -437 |\n",
+ "| StdTestEpRet | 268 |\n",
+ "| MaxTestEpRet | -19.2 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.67e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 48.5 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 46.2 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 86.8 |\n",
+ "| LossQ | 285 |\n",
+ "| Time | 2.14e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 77 |\n",
+ "| AverageEpRet | -468 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -125 |\n",
+ "| MinEpRet | -901 |\n",
+ "| AverageTestEpRet | -556 |\n",
+ "| StdTestEpRet | 262 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -812 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.69e+05 |\n",
+ "| AverageQ1Vals | -95.7 |\n",
+ "| StdQ1Vals | 153 |\n",
+ "| MaxQ1Vals | 37.2 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -95.7 |\n",
+ "| StdQ2Vals | 153 |\n",
+ "| MaxQ2Vals | 49.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.9 |\n",
+ "| LossQ | 283 |\n",
+ "| Time | 2.18e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 78 |\n",
+ "| AverageEpRet | -568 |\n",
+ "| StdEpRet | 284 |\n",
+ "| MaxEpRet | -123 |\n",
+ "| MinEpRet | -1.27e+03 |\n",
+ "| AverageTestEpRet | -550 |\n",
+ "| StdTestEpRet | 360 |\n",
+ "| MaxTestEpRet | -164 |\n",
+ "| MinTestEpRet | -1.26e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.72e+05 |\n",
+ "| AverageQ1Vals | -94.5 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 47.8 |\n",
+ "| MinQ1Vals | -1.2e+03 |\n",
+ "| AverageQ2Vals | -94.5 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 92 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 85.9 |\n",
+ "| LossQ | 265 |\n",
+ "| Time | 2.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 79 |\n",
+ "| AverageEpRet | -566 |\n",
+ "| StdEpRet | 337 |\n",
+ "| MaxEpRet | -96.5 |\n",
+ "| MinEpRet | -1.45e+03 |\n",
+ "| AverageTestEpRet | -543 |\n",
+ "| StdTestEpRet | 365 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -1.41e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.74e+05 |\n",
+ "| AverageQ1Vals | -94.8 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 69.7 |\n",
+ "| MinQ1Vals | -1.21e+03 |\n",
+ "| AverageQ2Vals | -94.8 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 75.6 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 278 |\n",
+ "| Time | 2.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 80 |\n",
+ "| AverageEpRet | -616 |\n",
+ "| StdEpRet | 290 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -1.16e+03 |\n",
+ "| AverageTestEpRet | -447 |\n",
+ "| StdTestEpRet | 196 |\n",
+ "| MaxTestEpRet | -182 |\n",
+ "| MinTestEpRet | -783 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.76e+05 |\n",
+ "| AverageQ1Vals | -94.6 |\n",
+ "| StdQ1Vals | 151 |\n",
+ "| MaxQ1Vals | 59.6 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -94.6 |\n",
+ "| StdQ2Vals | 151 |\n",
+ "| MaxQ2Vals | 74.6 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 86.4 |\n",
+ "| LossQ | 271 |\n",
+ "| Time | 2.28e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 81 |\n",
+ "| AverageEpRet | -535 |\n",
+ "| StdEpRet | 217 |\n",
+ "| MaxEpRet | -131 |\n",
+ "| MinEpRet | -999 |\n",
+ "| AverageTestEpRet | -422 |\n",
+ "| StdTestEpRet | 270 |\n",
+ "| MaxTestEpRet | -67.3 |\n",
+ "| MinTestEpRet | -1.09e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.78e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 150 |\n",
+ "| MaxQ1Vals | 47.4 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 150 |\n",
+ "| MaxQ2Vals | 71 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 85.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.31e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 82 |\n",
+ "| AverageEpRet | -505 |\n",
+ "| StdEpRet | 251 |\n",
+ "| MaxEpRet | -79.9 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -734 |\n",
+ "| StdTestEpRet | 289 |\n",
+ "| MaxTestEpRet | -227 |\n",
+ "| MinTestEpRet | -1.22e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.8e+05 |\n",
+ "| AverageQ1Vals | -94.2 |\n",
+ "| StdQ1Vals | 149 |\n",
+ "| MaxQ1Vals | 53 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -94.2 |\n",
+ "| StdQ2Vals | 149 |\n",
+ "| MaxQ2Vals | 67.3 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 86.6 |\n",
+ "| LossQ | 267 |\n",
+ "| Time | 2.35e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 83 |\n",
+ "| AverageEpRet | -585 |\n",
+ "| StdEpRet | 348 |\n",
+ "| MaxEpRet | -96.7 |\n",
+ "| MinEpRet | -1.3e+03 |\n",
+ "| AverageTestEpRet | -436 |\n",
+ "| StdTestEpRet | 169 |\n",
+ "| MaxTestEpRet | -212 |\n",
+ "| MinTestEpRet | -748 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.83e+05 |\n",
+ "| AverageQ1Vals | -92.9 |\n",
+ "| StdQ1Vals | 147 |\n",
+ "| MaxQ1Vals | 44.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -92.9 |\n",
+ "| StdQ2Vals | 147 |\n",
+ "| MaxQ2Vals | 75.2 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 84.8 |\n",
+ "| LossQ | 263 |\n",
+ "| Time | 2.38e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 84 |\n",
+ "| AverageEpRet | -406 |\n",
+ "| StdEpRet | 232 |\n",
+ "| MaxEpRet | -128 |\n",
+ "| MinEpRet | -924 |\n",
+ "| AverageTestEpRet | -650 |\n",
+ "| StdTestEpRet | 236 |\n",
+ "| MaxTestEpRet | -198 |\n",
+ "| MinTestEpRet | -1.01e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.85e+05 |\n",
+ "| AverageQ1Vals | -92.5 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 46.3 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -92.5 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 80.6 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 84.4 |\n",
+ "| LossQ | 258 |\n",
+ "| Time | 2.41e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 85 |\n",
+ "| AverageEpRet | -474 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -180 |\n",
+ "| MinEpRet | -1.22e+03 |\n",
+ "| AverageTestEpRet | -527 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -166 |\n",
+ "| MinTestEpRet | -737 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.87e+05 |\n",
+ "| AverageQ1Vals | -91.8 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 51.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -91.8 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 33.2 |\n",
+ "| MinQ2Vals | -1.06e+03 |\n",
+ "| LossPi | 84.1 |\n",
+ "| LossQ | 249 |\n",
+ "| Time | 2.44e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 86 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 264 |\n",
+ "| MaxEpRet | -112 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -665 |\n",
+ "| StdTestEpRet | 378 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.89e+05 |\n",
+ "| AverageQ1Vals | -91.6 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 65.2 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -91.7 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 67.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 83.5 |\n",
+ "| LossQ | 238 |\n",
+ "| Time | 2.47e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 87 |\n",
+ "| AverageEpRet | -515 |\n",
+ "| StdEpRet | 228 |\n",
+ "| MaxEpRet | -122 |\n",
+ "| MinEpRet | -976 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 202 |\n",
+ "| MaxTestEpRet | -180 |\n",
+ "| MinTestEpRet | -829 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.91e+05 |\n",
+ "| AverageQ1Vals | -92.1 |\n",
+ "| StdQ1Vals | 146 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -92.1 |\n",
+ "| StdQ2Vals | 146 |\n",
+ "| MaxQ2Vals | 54.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 83.6 |\n",
+ "| LossQ | 256 |\n",
+ "| Time | 2.51e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 88 |\n",
+ "| AverageEpRet | -572 |\n",
+ "| StdEpRet | 258 |\n",
+ "| MaxEpRet | -57 |\n",
+ "| MinEpRet | -1.07e+03 |\n",
+ "| AverageTestEpRet | -508 |\n",
+ "| StdTestEpRet | 163 |\n",
+ "| MaxTestEpRet | -217 |\n",
+ "| MinTestEpRet | -794 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.94e+05 |\n",
+ "| AverageQ1Vals | -91.3 |\n",
+ "| StdQ1Vals | 145 |\n",
+ "| MaxQ1Vals | 42.6 |\n",
+ "| MinQ1Vals | -1.12e+03 |\n",
+ "| AverageQ2Vals | -91.3 |\n",
+ "| StdQ2Vals | 145 |\n",
+ "| MaxQ2Vals | 58 |\n",
+ "| MinQ2Vals | -1.09e+03 |\n",
+ "| LossPi | 83.7 |\n",
+ "| LossQ | 246 |\n",
+ "| Time | 2.54e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 89 |\n",
+ "| AverageEpRet | -503 |\n",
+ "| StdEpRet | 241 |\n",
+ "| MaxEpRet | -49.9 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -562 |\n",
+ "| StdTestEpRet | 306 |\n",
+ "| MaxTestEpRet | -68.2 |\n",
+ "| MinTestEpRet | -1.15e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.96e+05 |\n",
+ "| AverageQ1Vals | -90.3 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 49.8 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -90.3 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 54.1 |\n",
+ "| MinQ2Vals | -1.15e+03 |\n",
+ "| LossPi | 83 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.57e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 90 |\n",
+ "| AverageEpRet | -478 |\n",
+ "| StdEpRet | 286 |\n",
+ "| MaxEpRet | -68 |\n",
+ "| MinEpRet | -1.15e+03 |\n",
+ "| AverageTestEpRet | -603 |\n",
+ "| StdTestEpRet | 390 |\n",
+ "| MaxTestEpRet | -123 |\n",
+ "| MinTestEpRet | -1.55e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 1.98e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.5 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.2 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.6 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.6e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 91 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 252 |\n",
+ "| MaxEpRet | -101 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -566 |\n",
+ "| StdTestEpRet | 308 |\n",
+ "| MaxTestEpRet | -62.9 |\n",
+ "| MinTestEpRet | -1.02e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2e+05 |\n",
+ "| AverageQ1Vals | -89.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36.4 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -89.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 25.5 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 82.7 |\n",
+ "| LossQ | 241 |\n",
+ "| Time | 2.63e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 92 |\n",
+ "| AverageEpRet | -425 |\n",
+ "| StdEpRet | 205 |\n",
+ "| MaxEpRet | -170 |\n",
+ "| MinEpRet | -998 |\n",
+ "| AverageTestEpRet | -536 |\n",
+ "| StdTestEpRet | 271 |\n",
+ "| MaxTestEpRet | -209 |\n",
+ "| MinTestEpRet | -1.04e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.02e+05 |\n",
+ "| AverageQ1Vals | -88.7 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 36 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -88.7 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 32.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 81.3 |\n",
+ "| LossQ | 237 |\n",
+ "| Time | 2.67e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 93 |\n",
+ "| AverageEpRet | -498 |\n",
+ "| StdEpRet | 277 |\n",
+ "| MaxEpRet | -83.8 |\n",
+ "| MinEpRet | -1.11e+03 |\n",
+ "| AverageTestEpRet | -523 |\n",
+ "| StdTestEpRet | 286 |\n",
+ "| MaxTestEpRet | -83.5 |\n",
+ "| MinTestEpRet | -967 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.05e+05 |\n",
+ "| AverageQ1Vals | -88.2 |\n",
+ "| StdQ1Vals | 144 |\n",
+ "| MaxQ1Vals | 97.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -88.2 |\n",
+ "| StdQ2Vals | 144 |\n",
+ "| MaxQ2Vals | 36.8 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 80.8 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.7e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 94 |\n",
+ "| AverageEpRet | -470 |\n",
+ "| StdEpRet | 231 |\n",
+ "| MaxEpRet | -88.9 |\n",
+ "| MinEpRet | -946 |\n",
+ "| AverageTestEpRet | -407 |\n",
+ "| StdTestEpRet | 194 |\n",
+ "| MaxTestEpRet | -167 |\n",
+ "| MinTestEpRet | -793 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.07e+05 |\n",
+ "| AverageQ1Vals | -87.9 |\n",
+ "| StdQ1Vals | 143 |\n",
+ "| MaxQ1Vals | 48.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -87.9 |\n",
+ "| StdQ2Vals | 143 |\n",
+ "| MaxQ2Vals | 65.6 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 80.4 |\n",
+ "| LossQ | 244 |\n",
+ "| Time | 2.73e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 95 |\n",
+ "| AverageEpRet | -471 |\n",
+ "| StdEpRet | 257 |\n",
+ "| MaxEpRet | -113 |\n",
+ "| MinEpRet | -994 |\n",
+ "| AverageTestEpRet | -563 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -261 |\n",
+ "| MinTestEpRet | -827 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.09e+05 |\n",
+ "| AverageQ1Vals | -87.2 |\n",
+ "| StdQ1Vals | 141 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -87.2 |\n",
+ "| StdQ2Vals | 141 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 80.3 |\n",
+ "| LossQ | 243 |\n",
+ "| Time | 2.76e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 96 |\n",
+ "| AverageEpRet | -473 |\n",
+ "| StdEpRet | 268 |\n",
+ "| MaxEpRet | -76.1 |\n",
+ "| MinEpRet | -1.03e+03 |\n",
+ "| AverageTestEpRet | -392 |\n",
+ "| StdTestEpRet | 191 |\n",
+ "| MaxTestEpRet | -90.2 |\n",
+ "| MinTestEpRet | -713 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.11e+05 |\n",
+ "| AverageQ1Vals | -86.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 32.9 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -86.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 40.5 |\n",
+ "| MinQ2Vals | -1.1e+03 |\n",
+ "| LossPi | 79.5 |\n",
+ "| LossQ | 233 |\n",
+ "| Time | 2.79e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 97 |\n",
+ "| AverageEpRet | -497 |\n",
+ "| StdEpRet | 345 |\n",
+ "| MaxEpRet | -37.1 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -420 |\n",
+ "| StdTestEpRet | 227 |\n",
+ "| MaxTestEpRet | -72.6 |\n",
+ "| MinTestEpRet | -742 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.13e+05 |\n",
+ "| AverageQ1Vals | -86.5 |\n",
+ "| StdQ1Vals | 140 |\n",
+ "| MaxQ1Vals | 43 |\n",
+ "| MinQ1Vals | -1.17e+03 |\n",
+ "| AverageQ2Vals | -86.5 |\n",
+ "| StdQ2Vals | 140 |\n",
+ "| MaxQ2Vals | 38.9 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 79.6 |\n",
+ "| LossQ | 234 |\n",
+ "| Time | 2.82e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 98 |\n",
+ "| AverageEpRet | -429 |\n",
+ "| StdEpRet | 215 |\n",
+ "| MaxEpRet | -135 |\n",
+ "| MinEpRet | -848 |\n",
+ "| AverageTestEpRet | -511 |\n",
+ "| StdTestEpRet | 337 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -1.37e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.16e+05 |\n",
+ "| AverageQ1Vals | -86.1 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.3 |\n",
+ "| MinQ1Vals | -1.25e+03 |\n",
+ "| AverageQ2Vals | -86.1 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43.7 |\n",
+ "| MinQ2Vals | -1.21e+03 |\n",
+ "| LossPi | 79.1 |\n",
+ "| LossQ | 226 |\n",
+ "| Time | 2.85e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 99 |\n",
+ "| AverageEpRet | -514 |\n",
+ "| StdEpRet | 278 |\n",
+ "| MaxEpRet | -141 |\n",
+ "| MinEpRet | -1.21e+03 |\n",
+ "| AverageTestEpRet | -633 |\n",
+ "| StdTestEpRet | 489 |\n",
+ "| MaxTestEpRet | -181 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.18e+05 |\n",
+ "| AverageQ1Vals | -84.8 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 30.2 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -84.8 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 27.5 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 77.7 |\n",
+ "| LossQ | 225 |\n",
+ "| Time | 2.88e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 100 |\n",
+ "| AverageEpRet | -557 |\n",
+ "| StdEpRet | 280 |\n",
+ "| MaxEpRet | -26 |\n",
+ "| MinEpRet | -1.17e+03 |\n",
+ "| AverageTestEpRet | -544 |\n",
+ "| StdTestEpRet | 372 |\n",
+ "| MaxTestEpRet | -148 |\n",
+ "| MinTestEpRet | -1.24e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.2e+05 |\n",
+ "| AverageQ1Vals | -84 |\n",
+ "| StdQ1Vals | 139 |\n",
+ "| MaxQ1Vals | 51.4 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -84 |\n",
+ "| StdQ2Vals | 139 |\n",
+ "| MaxQ2Vals | 43 |\n",
+ "| MinQ2Vals | -1.22e+03 |\n",
+ "| LossPi | 76.6 |\n",
+ "| LossQ | 220 |\n",
+ "| Time | 2.92e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 101 |\n",
+ "| AverageEpRet | -434 |\n",
+ "| StdEpRet | 192 |\n",
+ "| MaxEpRet | -155 |\n",
+ "| MinEpRet | -806 |\n",
+ "| AverageTestEpRet | -418 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -244 |\n",
+ "| MinTestEpRet | -730 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.22e+05 |\n",
+ "| AverageQ1Vals | -82.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 35.3 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -82.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 54.9 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 76.3 |\n",
+ "| LossQ | 219 |\n",
+ "| Time | 2.95e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 102 |\n",
+ "| AverageEpRet | -625 |\n",
+ "| StdEpRet | 341 |\n",
+ "| MaxEpRet | -120 |\n",
+ "| MinEpRet | -1.24e+03 |\n",
+ "| AverageTestEpRet | -490 |\n",
+ "| StdTestEpRet | 223 |\n",
+ "| MaxTestEpRet | -73.4 |\n",
+ "| MinTestEpRet | -824 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.24e+05 |\n",
+ "| AverageQ1Vals | -81.2 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 96.8 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -81.2 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 71.4 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 74.2 |\n",
+ "| LossQ | 213 |\n",
+ "| Time | 2.99e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 103 |\n",
+ "| AverageEpRet | -617 |\n",
+ "| StdEpRet | 327 |\n",
+ "| MaxEpRet | -104 |\n",
+ "| MinEpRet | -1.32e+03 |\n",
+ "| AverageTestEpRet | -561 |\n",
+ "| StdTestEpRet | 214 |\n",
+ "| MaxTestEpRet | -248 |\n",
+ "| MinTestEpRet | -906 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.27e+05 |\n",
+ "| AverageQ1Vals | -80.7 |\n",
+ "| StdQ1Vals | 138 |\n",
+ "| MaxQ1Vals | 70.5 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -80.7 |\n",
+ "| StdQ2Vals | 138 |\n",
+ "| MaxQ2Vals | 41 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 74.1 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.02e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 104 |\n",
+ "| AverageEpRet | -527 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -65.7 |\n",
+ "| MinEpRet | -1.01e+03 |\n",
+ "| AverageTestEpRet | -425 |\n",
+ "| StdTestEpRet | 224 |\n",
+ "| MaxTestEpRet | -92.5 |\n",
+ "| MinTestEpRet | -774 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.29e+05 |\n",
+ "| AverageQ1Vals | -79.9 |\n",
+ "| StdQ1Vals | 137 |\n",
+ "| MaxQ1Vals | 41.1 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -79.9 |\n",
+ "| StdQ2Vals | 137 |\n",
+ "| MaxQ2Vals | 44.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 73.2 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.05e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 105 |\n",
+ "| AverageEpRet | -469 |\n",
+ "| StdEpRet | 235 |\n",
+ "| MaxEpRet | -144 |\n",
+ "| MinEpRet | -1.12e+03 |\n",
+ "| AverageTestEpRet | -317 |\n",
+ "| StdTestEpRet | 137 |\n",
+ "| MaxTestEpRet | -134 |\n",
+ "| MinTestEpRet | -541 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.31e+05 |\n",
+ "| AverageQ1Vals | -78.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 24.5 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -78.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 38.7 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.6 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.08e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 106 |\n",
+ "| AverageEpRet | -509 |\n",
+ "| StdEpRet | 244 |\n",
+ "| MaxEpRet | -114 |\n",
+ "| MinEpRet | -989 |\n",
+ "| AverageTestEpRet | -542 |\n",
+ "| StdTestEpRet | 277 |\n",
+ "| MaxTestEpRet | -129 |\n",
+ "| MinTestEpRet | -965 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.33e+05 |\n",
+ "| AverageQ1Vals | -79.1 |\n",
+ "| StdQ1Vals | 136 |\n",
+ "| MaxQ1Vals | 40.6 |\n",
+ "| MinQ1Vals | -1.09e+03 |\n",
+ "| AverageQ2Vals | -79.1 |\n",
+ "| StdQ2Vals | 136 |\n",
+ "| MaxQ2Vals | 54 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 72.7 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.11e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 107 |\n",
+ "| AverageEpRet | -571 |\n",
+ "| StdEpRet | 245 |\n",
+ "| MaxEpRet | -189 |\n",
+ "| MinEpRet | -969 |\n",
+ "| AverageTestEpRet | -538 |\n",
+ "| StdTestEpRet | 189 |\n",
+ "| MaxTestEpRet | -255 |\n",
+ "| MinTestEpRet | -880 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.35e+05 |\n",
+ "| AverageQ1Vals | -78.4 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 33.8 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -78.4 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 45.3 |\n",
+ "| MinQ2Vals | -1.16e+03 |\n",
+ "| LossPi | 71.6 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.15e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 108 |\n",
+ "| AverageEpRet | -481 |\n",
+ "| StdEpRet | 187 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -837 |\n",
+ "| AverageTestEpRet | -399 |\n",
+ "| StdTestEpRet | 162 |\n",
+ "| MaxTestEpRet | -149 |\n",
+ "| MinTestEpRet | -668 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.38e+05 |\n",
+ "| AverageQ1Vals | -78 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 34 |\n",
+ "| MinQ1Vals | -1.15e+03 |\n",
+ "| AverageQ2Vals | -78 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.9 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 72.1 |\n",
+ "| LossQ | 217 |\n",
+ "| Time | 3.18e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 109 |\n",
+ "| AverageEpRet | -476 |\n",
+ "| StdEpRet | 272 |\n",
+ "| MaxEpRet | -63.6 |\n",
+ "| MinEpRet | -1.04e+03 |\n",
+ "| AverageTestEpRet | -531 |\n",
+ "| StdTestEpRet | 326 |\n",
+ "| MaxTestEpRet | -90.1 |\n",
+ "| MinTestEpRet | -1.19e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.4e+05 |\n",
+ "| AverageQ1Vals | -76.9 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 59 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.9 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 42.2 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.7 |\n",
+ "| LossQ | 212 |\n",
+ "| Time | 3.21e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 110 |\n",
+ "| AverageEpRet | -523 |\n",
+ "| StdEpRet | 321 |\n",
+ "| MaxEpRet | -29.2 |\n",
+ "| MinEpRet | -1.39e+03 |\n",
+ "| AverageTestEpRet | -583 |\n",
+ "| StdTestEpRet | 282 |\n",
+ "| MaxTestEpRet | -187 |\n",
+ "| MinTestEpRet | -1.03e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.42e+05 |\n",
+ "| AverageQ1Vals | -76.8 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 43.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.8 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 53.2 |\n",
+ "| MinQ2Vals | -1.19e+03 |\n",
+ "| LossPi | 70.9 |\n",
+ "| LossQ | 202 |\n",
+ "| Time | 3.24e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 111 |\n",
+ "| AverageEpRet | -453 |\n",
+ "| StdEpRet | 254 |\n",
+ "| MaxEpRet | -81.5 |\n",
+ "| MinEpRet | -1.13e+03 |\n",
+ "| AverageTestEpRet | -524 |\n",
+ "| StdTestEpRet | 105 |\n",
+ "| MaxTestEpRet | -339 |\n",
+ "| MinTestEpRet | -689 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.44e+05 |\n",
+ "| AverageQ1Vals | -76.5 |\n",
+ "| StdQ1Vals | 135 |\n",
+ "| MaxQ1Vals | 30.1 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.5 |\n",
+ "| StdQ2Vals | 135 |\n",
+ "| MaxQ2Vals | 41.8 |\n",
+ "| MinQ2Vals | -1.23e+03 |\n",
+ "| LossPi | 70.4 |\n",
+ "| LossQ | 207 |\n",
+ "| Time | 3.27e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 112 |\n",
+ "| AverageEpRet | -596 |\n",
+ "| StdEpRet | 262 |\n",
+ "| MaxEpRet | -173 |\n",
+ "| MinEpRet | -1.26e+03 |\n",
+ "| AverageTestEpRet | -415 |\n",
+ "| StdTestEpRet | 161 |\n",
+ "| MaxTestEpRet | -137 |\n",
+ "| MinTestEpRet | -652 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.46e+05 |\n",
+ "| AverageQ1Vals | -76.3 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 34.9 |\n",
+ "| MinQ1Vals | -1.19e+03 |\n",
+ "| AverageQ2Vals | -76.3 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 46.7 |\n",
+ "| MinQ2Vals | -1.18e+03 |\n",
+ "| LossPi | 70.3 |\n",
+ "| LossQ | 198 |\n",
+ "| Time | 3.3e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 113 |\n",
+ "| AverageEpRet | -482 |\n",
+ "| StdEpRet | 333 |\n",
+ "| MaxEpRet | -62.8 |\n",
+ "| MinEpRet | -1.44e+03 |\n",
+ "| AverageTestEpRet | -559 |\n",
+ "| StdTestEpRet | 304 |\n",
+ "| MaxTestEpRet | -142 |\n",
+ "| MinTestEpRet | -1.28e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.49e+05 |\n",
+ "| AverageQ1Vals | -76.2 |\n",
+ "| StdQ1Vals | 134 |\n",
+ "| MaxQ1Vals | 38.3 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -76.2 |\n",
+ "| StdQ2Vals | 134 |\n",
+ "| MaxQ2Vals | 50.3 |\n",
+ "| MinQ2Vals | -1.2e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 200 |\n",
+ "| Time | 3.33e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 114 |\n",
+ "| AverageEpRet | -450 |\n",
+ "| StdEpRet | 216 |\n",
+ "| MaxEpRet | -94.9 |\n",
+ "| MinEpRet | -872 |\n",
+ "| AverageTestEpRet | -432 |\n",
+ "| StdTestEpRet | 132 |\n",
+ "| MaxTestEpRet | -232 |\n",
+ "| MinTestEpRet | -645 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.51e+05 |\n",
+ "| AverageQ1Vals | -75.7 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 44.9 |\n",
+ "| MinQ1Vals | -1.16e+03 |\n",
+ "| AverageQ2Vals | -75.7 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 44.4 |\n",
+ "| MinQ2Vals | -1.13e+03 |\n",
+ "| LossPi | 69.6 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.36e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 115 |\n",
+ "| AverageEpRet | -487 |\n",
+ "| StdEpRet | 240 |\n",
+ "| MaxEpRet | -108 |\n",
+ "| MinEpRet | -932 |\n",
+ "| AverageTestEpRet | -371 |\n",
+ "| StdTestEpRet | 152 |\n",
+ "| MaxTestEpRet | -143 |\n",
+ "| MinTestEpRet | -616 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.53e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 21.2 |\n",
+ "| MinQ1Vals | -1.14e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 29.5 |\n",
+ "| MinQ2Vals | -1.11e+03 |\n",
+ "| LossPi | 69.7 |\n",
+ "| LossQ | 205 |\n",
+ "| Time | 3.4e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 116 |\n",
+ "| AverageEpRet | -538 |\n",
+ "| StdEpRet | 220 |\n",
+ "| MaxEpRet | -110 |\n",
+ "| MinEpRet | -960 |\n",
+ "| AverageTestEpRet | -475 |\n",
+ "| StdTestEpRet | 311 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -1.23e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.55e+05 |\n",
+ "| AverageQ1Vals | -75.4 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 26 |\n",
+ "| MinQ1Vals | -1.18e+03 |\n",
+ "| AverageQ2Vals | -75.4 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 37.1 |\n",
+ "| MinQ2Vals | -1.17e+03 |\n",
+ "| LossPi | 69.3 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.43e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 117 |\n",
+ "| AverageEpRet | -437 |\n",
+ "| StdEpRet | 203 |\n",
+ "| MaxEpRet | -92.8 |\n",
+ "| MinEpRet | -857 |\n",
+ "| AverageTestEpRet | -582 |\n",
+ "| StdTestEpRet | 247 |\n",
+ "| MaxTestEpRet | -79.8 |\n",
+ "| MinTestEpRet | -861 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.57e+05 |\n",
+ "| AverageQ1Vals | -75.5 |\n",
+ "| StdQ1Vals | 132 |\n",
+ "| MaxQ1Vals | 49.5 |\n",
+ "| MinQ1Vals | -1.13e+03 |\n",
+ "| AverageQ2Vals | -75.5 |\n",
+ "| StdQ2Vals | 132 |\n",
+ "| MaxQ2Vals | 51 |\n",
+ "| MinQ2Vals | -1.14e+03 |\n",
+ "| LossPi | 69.4 |\n",
+ "| LossQ | 201 |\n",
+ "| Time | 3.47e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "---------------------------------------\n",
+ "| Epoch | 118 |\n",
+ "| AverageEpRet | -549 |\n",
+ "| StdEpRet | 236 |\n",
+ "| MaxEpRet | -111 |\n",
+ "| MinEpRet | -1.28e+03 |\n",
+ "| AverageTestEpRet | -588 |\n",
+ "| StdTestEpRet | 404 |\n",
+ "| MaxTestEpRet | -138 |\n",
+ "| MinTestEpRet | -1.66e+03 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.6e+05 |\n",
+ "| AverageQ1Vals | -75.1 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 31.7 |\n",
+ "| MinQ1Vals | -1.05e+03 |\n",
+ "| AverageQ2Vals | -75.1 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 38.6 |\n",
+ "| MinQ2Vals | -1.05e+03 |\n",
+ "| LossPi | 69 |\n",
+ "| LossQ | 192 |\n",
+ "| Time | 3.5e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 119 |\n",
+ "| AverageEpRet | -491 |\n",
+ "| StdEpRet | 247 |\n",
+ "| MaxEpRet | -102 |\n",
+ "| MinEpRet | -945 |\n",
+ "| AverageTestEpRet | -535 |\n",
+ "| StdTestEpRet | 217 |\n",
+ "| MaxTestEpRet | -173 |\n",
+ "| MinTestEpRet | -879 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.62e+05 |\n",
+ "| AverageQ1Vals | -74.9 |\n",
+ "| StdQ1Vals | 130 |\n",
+ "| MaxQ1Vals | 60.9 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.9 |\n",
+ "| StdQ2Vals | 130 |\n",
+ "| MaxQ2Vals | 28.6 |\n",
+ "| MinQ2Vals | -1.12e+03 |\n",
+ "| LossPi | 69.2 |\n",
+ "| LossQ | 204 |\n",
+ "| Time | 3.53e+03 |\n",
+ "---------------------------------------\n",
+ "---------------------------------------\n",
+ "| Epoch | 120 |\n",
+ "| AverageEpRet | -431 |\n",
+ "| StdEpRet | 249 |\n",
+ "| MaxEpRet | -64.6 |\n",
+ "| MinEpRet | -885 |\n",
+ "| AverageTestEpRet | -519 |\n",
+ "| StdTestEpRet | 242 |\n",
+ "| MaxTestEpRet | -56.1 |\n",
+ "| MinTestEpRet | -838 |\n",
+ "| EpLen | 110 |\n",
+ "| TestEpLen | 110 |\n",
+ "| TotalEnvInteracts | 2.64e+05 |\n",
+ "| AverageQ1Vals | -74.8 |\n",
+ "| StdQ1Vals | 129 |\n",
+ "| MaxQ1Vals | 105 |\n",
+ "| MinQ1Vals | -1.11e+03 |\n",
+ "| AverageQ2Vals | -74.8 |\n",
+ "| StdQ2Vals | 129 |\n",
+ "| MaxQ2Vals | 77.4 |\n",
+ "| MinQ2Vals | -1.08e+03 |\n",
+ "| LossPi | 69.1 |\n",
+ "| LossQ | 197 |\n",
+ "| Time | 3.56e+03 |\n",
+ "---------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Setup experiment\n",
+ "logger_kwargs = dict(output_dir='model', exp_name='v0')\n",
+ "\n",
+ "# Run training\n",
+ "spinup.td3_pytorch(GyroscopeEnv, seed=0, steps_per_epoch=110*20, epochs=120, replay_size=1000000, gamma=0.95,\n",
+ "polyak=0.995, batch_size=100, start_steps=20000, update_after=800, update_every=50,max_ep_len=110,logger_kwargs=logger_kwargs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Gn3Gp40bcOVz"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 972,
+ "status": "ok",
+ "timestamp": 1584036455886,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "6GyY0wE-QBOj",
+ "outputId": "9a5e9011-e024-4a65-8383-83c062cdcad9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-900.18321242575\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "env = GyroscopeEnv()\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = torch.load('model/pyt_save/model.pt')\n",
+ "\n",
+ "# Test parameters\n",
+ "x1,x2,x3,x4,x1_ref,x3_ref,w = 0,1,0,1,1,3,25\n",
+ "state = env.reset(np.array([x1,x2,x3,x4,x1_ref,x3_ref,w]))\n",
+ "val = []\n",
+ "act = []\n",
+ "dt = 0.01\n",
+ "time = np.arange(0, 4, dt)\n",
+ "score = 0\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " action = agent.act(torch.as_tensor(state, dtype=torch.float32))\n",
+ " act.append(action)\n",
+ " state, reward, done, _ = env.step(action)\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ "\n",
+ "env.close()\n",
+ "print(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "vvMjuRHDcfrE"
+ },
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 1856,
+ "status": "ok",
+ "timestamp": 1584036457424,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "aCZCqujgcMVA",
+ "outputId": "05490294-aab8-4933-ca9b-e13ba85ecf6d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2160x2160 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(4,2,figsize=(30,30))\n",
+ "plt.subplot(4,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "plt.plot(time, [row[4] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,2)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "plt.plot(time, [row[5] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(4,2,3)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[0]- row[4]) for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[angle_normalize(row[2]- row[5]) for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(4,2,7)\n",
+ "plt.title('Red gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u1 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in act],'r-')\n",
+ "\n",
+ "plt.subplot(4,2,8)\n",
+ "plt.title('Blue gimbal input',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'u2 (V)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in act],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fz9r-gaStzpk"
+ },
+ "source": [
+ "## 3D rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "HQM1E8JYc-cC"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div id=\"glowscript\" class=\"glowscript\"></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 400\n",
+ "\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "gyroscope_ddpg_testing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/training_spinuplib/model/config.json b/code/training_spinuplib/model/config.json
new file mode 100644
index 0000000..a5fcbfc
--- /dev/null
+++ b/code/training_spinuplib/model/config.json
@@ -0,0 +1,44 @@
+{
+ "ac_kwargs": {},
+ "act_noise": 0.1,
+ "actor_critic": "MLPActorCritic",
+ "batch_size": 100,
+ "env_fn": "GyroscopeEnv",
+ "epochs": 120,
+ "exp_name": "v0",
+ "gamma": 0.95,
+ "logger": {
+ "<spinup.utils.logx.EpochLogger object at 0x7f7e800ca588>": {
+ "epoch_dict": {},
+ "exp_name": "v0",
+ "first_row": true,
+ "log_current_row": {},
+ "log_headers": [],
+ "output_dir": "model",
+ "output_file": {
+ "<_io.TextIOWrapper name='model/progress.txt' mode='w' encoding='UTF-8'>": {
+ "mode": "w"
+ }
+ }
+ }
+ },
+ "logger_kwargs": {
+ "exp_name": "v0",
+ "output_dir": "model"
+ },
+ "max_ep_len": 110,
+ "noise_clip": 0.5,
+ "num_test_episodes": 10,
+ "pi_lr": 0.001,
+ "policy_delay": 2,
+ "polyak": 0.995,
+ "q_lr": 0.001,
+ "replay_size": 1000000,
+ "save_freq": 1,
+ "seed": 0,
+ "start_steps": 20000,
+ "steps_per_epoch": 2200,
+ "target_noise": 0.2,
+ "update_after": 800,
+ "update_every": 50
+}
\ No newline at end of file
diff --git a/code/training_spinuplib/model/progress.txt b/code/training_spinuplib/model/progress.txt
new file mode 100644
index 0000000..2342e54
--- /dev/null
+++ b/code/training_spinuplib/model/progress.txt
@@ -0,0 +1,121 @@
+Epoch AverageEpRet StdEpRet MaxEpRet MinEpRet AverageTestEpRet StdTestEpRet MaxTestEpRet MinTestEpRet EpLen TestEpLen TotalEnvInteracts AverageQ1Vals StdQ1Vals MaxQ1Vals MinQ1Vals AverageQ2Vals StdQ2Vals MaxQ2Vals MinQ2Vals LossPi LossQ Time
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diff --git a/code/training_spinuplib/model/pyt_save/model.pt b/code/training_spinuplib/model/pyt_save/model.pt
new file mode 100644
index 0000000..551f559
Binary files /dev/null and b/code/training_spinuplib/model/pyt_save/model.pt differ
diff --git a/code/training_spinuplib/model/vars.pkl b/code/training_spinuplib/model/vars.pkl
new file mode 100644
index 0000000..85496bf
Binary files /dev/null and b/code/training_spinuplib/model/vars.pkl differ
diff --git a/code/training_udalib/.ipynb_checkpoints/gyroscope_training_udalib-checkpoint.ipynb b/code/training_udalib/.ipynb_checkpoints/gyroscope_training_udalib-checkpoint.ipynb
new file mode 100644
index 0000000..ff2a3c7
--- /dev/null
+++ b/code/training_udalib/.ipynb_checkpoints/gyroscope_training_udalib-checkpoint.ipynb
@@ -0,0 +1,685 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "x83dMPapQBN6"
+ },
+ "source": [
+ "# Gyroscope DDPG training (udacity library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8inmkEG5QHqx"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 655,
+ "status": "ok",
+ "timestamp": 1584030129316,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "DnnJGcnsQvZC",
+ "outputId": "9d6d1408-da29-44ed-af74-4ebdc2bd865a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/Colab Notebooks/DDPG_uda_v0\n"
+ ]
+ }
+ ],
+ "source": [
+ "cd drive/My\\ Drive/Colab Notebooks/DDPG_uda_v0/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "fuJhdd479TpP"
+ },
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'torch'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-d1dc844787f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msolve_ivp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeque\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
+ ]
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from collections import deque\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "\n",
+ "\n",
+ "#from vpython import *\n",
+ "from ddpg_agent import Agent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "O0O0t5ZR9Tp6"
+ },
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Al_k1rtvQBOM"
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self, dt):\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = dt\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 40 * 2 * np.pi / 60\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiskSpeed = 300 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxAngle,self.maxAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, x1_ref, x3_ref, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " # Option 1 for reward:\n",
+ " reward = 0.5*(angle_normalize(x1 - x1_ref)**2 + angle_normalize(x3 - x3_ref)**2) #+ .001*(x2**2) + .001*(x4**2) + .001*(u1**2) + .001*(u2**2)\n",
+ "\n",
+ " # Option 2 for reward:\n",
+ " \n",
+ " \"\"\"if diff_x1**2<0.01 and diff_x3**2<0.01:\n",
+ " reward = 0\n",
+ " else:\n",
+ " reward = 1\"\"\"\n",
+ "\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,x1_ref, x3_ref,w])\n",
+ "\n",
+ " return (self.state, -reward, False, {})\n",
+ "\n",
+ " def reset(self, state = None):\n",
+ " \n",
+ " \n",
+ " # Generate random state (for training) or use given state (for simulation)\n",
+ " if state is None:\n",
+ " self.state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " self.state = state\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = x[-1]\n",
+ "\n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "2YB2o73h9TrC"
+ },
+ "source": [
+ "## DDPG run"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "uo5HKwz29TrF"
+ },
+ "outputs": [],
+ "source": [
+ "def ddpg(n_episodes=5000, max_t=250, print_every=100):\n",
+ " scores_deque = deque(maxlen=print_every)\n",
+ " scores = []\n",
+ " for i_episode in range(1, n_episodes+1):\n",
+ " state = env.reset(None)\n",
+ " agent.reset()\n",
+ " score = 0\n",
+ " for t in range(max_t):\n",
+ " action = agent.act(state)\n",
+ " next_state, reward, done, _ = env.step(action)\n",
+ " agent.step(state, action, reward, next_state, done)\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ " scores_deque.append(score)\n",
+ " scores.append(score)\n",
+ " print('\\rEpisode {}\\tScore: {:.2f}'.format(i_episode, score), end=\"\")\n",
+ " torch.save(agent.actor_local.state_dict(), 'checkpoint_actor.pth')\n",
+ " torch.save(agent.critic_local.state_dict(), 'checkpoint_critic.pth')\n",
+ " torch.save(agent.actor_optimizer.state_dict(), 'checkpoint_actor_opt.pth')\n",
+ " torch.save(agent.critic_optimizer.state_dict(), 'checkpoint_critic_opt.pth')\n",
+ " if i_episode % print_every == 0:\n",
+ " print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
+ " \n",
+ " return scores\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "be0wYIeBQBOc"
+ },
+ "source": [
+ "## Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 533
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 654004,
+ "status": "error",
+ "timestamp": 1584037207187,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "fLyFHs0yQBOd",
+ "outputId": "260489ff-5e40-416a-e529-5a0cfcaefceb"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n",
+ "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
+ " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Episode 100\tAverage Score: -2418.53\n",
+ "Episode 200\tAverage Score: -1813.93\n",
+ "Episode 300\tAverage Score: -1814.60\n",
+ "Episode 341\tScore: -1640.05"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "ignored",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-53-e407b799b307>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m agent.critic_optimizer.load_state_dict(checkpoint_critic_optimizer)\"\"\"\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mddpg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m<ipython-input-52-dcf8a51d0fa2>\u001b[0m in \u001b[0;36mddpg\u001b[0;34m(n_episodes, max_t, print_every)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/content/drive/My Drive/Colab Notebooks/DDPG_uda_v0/ddpg_agent.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, state, action, reward, next_state, done)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0mexperiences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperiences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGAMMA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_noise\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/content/drive/My Drive/Colab Notebooks/DDPG_uda_v0/ddpg_agent.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, experiences, gamma)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mcritic_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# ---------------------------- update actor ---------------------------- #\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_exp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias_correction2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'eps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias_correction2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'eps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0mstep_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mbias_correction1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "dt = 0.05\n",
+ "env = GyroscopeEnv(dt)\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = Agent(state_size=7, action_size=2, random_seed=2)\n",
+ "\n",
+ "\"\"\"# Access previous checkpoint\n",
+ "checkpoint_actor = torch.load('checkpoint_actor.pth')\n",
+ "checkpoint_critic = torch.load('checkpoint_critic.pth')\n",
+ "checkpoint_actor_optimizer = torch.load('checkpoint_actor_opt.pth')\n",
+ "checkpoint_critic_optimizer = torch.load('checkpoint_critic_opt.pth')\n",
+ "\n",
+ "# Use previous checkpoint\n",
+ "agent.actor_local.load_state_dict(checkpoint_actor)\n",
+ "agent.critic_local.load_state_dict(checkpoint_critic)\n",
+ "agent.actor_optimizer.load_state_dict(checkpoint_actor_optimizer)\n",
+ "agent.critic_optimizer.load_state_dict(checkpoint_critic_optimizer)\"\"\"\n",
+ "\n",
+ "scores = ddpg()\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111)\n",
+ "plt.plot(np.arange(1, len(scores)+1), scores)\n",
+ "plt.ylabel('Score')\n",
+ "plt.xlabel('Episode #')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Gn3Gp40bcOVz"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 972,
+ "status": "ok",
+ "timestamp": 1584036455886,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "6GyY0wE-QBOj",
+ "outputId": "9a5e9011-e024-4a65-8383-83c062cdcad9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n",
+ "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
+ " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-84.65790332698876\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "dt = 0.05\n",
+ "env = GyroscopeEnv(dt)\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = Agent(state_size=7, action_size=2, random_seed=2)\n",
+ "agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))\n",
+ "agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))\n",
+ "\n",
+ "# Test parameters\n",
+ "x1,x2,x3,x4,x1_ref,x3_ref,w = 2,0,0,0,1,1.22,10\n",
+ "state = env.reset(np.array([x1,x2,x3,x4,x1_ref,x3_ref,w]))\n",
+ "val = []\n",
+ "dt = 0.05\n",
+ "time = np.arange(0, 30, dt)\n",
+ "score = 0\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " action = agent.act(state, add_noise=False)\n",
+ " state, reward, done, _ = env.step(action)\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ "\n",
+ "env.close()\n",
+ "print(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "vvMjuRHDcfrE"
+ },
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 1856,
+ "status": "ok",
+ "timestamp": 1584036457424,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "aCZCqujgcMVA",
+ "outputId": "05490294-aab8-4933-ca9b-e13ba85ecf6d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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zrICRJEnjWlOnIC1fXpIvEycO/1ySJI0RJmCaYfp0K2AkSdK41vQKGNd/kSR1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ACuvnri25IkaToxATMOa9eWZxMwkiRpUHSrBGnBAth1VxMwkqTB0/cETEQcHxHXRsSyiHhrk/ff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\nkiRJkiSNmB0wkiRJkiRJI/Y/r6iEX/XdyloAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 1368x1368 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(3,2,figsize=(19,19))\n",
+ "plt.subplot(3,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "plt.plot(time, [row[4] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(3,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "plt.plot(time, [row[5] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(3,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[(row[0]- row[4]) for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[(row[2]- row[5]) for row in val],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fz9r-gaStzpk"
+ },
+ "source": [
+ "## 3D rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "HQM1E8JYc-cC"
+ },
+ "outputs": [],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "t = 0\n",
+ "dt = 0.01\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 200\n",
+ "\n",
+ "# 6.4/100\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "gyroscope_ddpg_testing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/training_udalib/README.md b/code/training_udalib/README.md
new file mode 100644
index 0000000..07fc79b
--- /dev/null
+++ b/code/training_udalib/README.md
@@ -0,0 +1,13 @@
+[//]: # (Image References)
+
+[image1]: https://user-images.githubusercontent.com/10624937/42135610-c37e0292-7d12-11e8-8228-4d3585f8c026.gif "Trained Agent"
+
+# Actor-Critic Methods
+
+### Instructions
+
+Open `DDPG.ipynb` to see an implementation of DDPG with OpenAI Gym's Pendulum environment.
+
+### Results
+
+![Trained Agent][image1]
\ No newline at end of file
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diff --git a/code/training_udalib/ddpg_agent.py b/code/training_udalib/ddpg_agent.py
new file mode 100644
index 0000000..9562705
--- /dev/null
+++ b/code/training_udalib/ddpg_agent.py
@@ -0,0 +1,189 @@
+import numpy as np
+import random
+import copy
+from collections import namedtuple, deque
+
+from model import Actor, Critic
+
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+
+BUFFER_SIZE = int(1e5) # replay buffer size
+BATCH_SIZE = 128 # minibatch size
+GAMMA = 0.99 # discount factor
+TAU = 1e-3 # for soft update of target parameters
+LR_ACTOR = 1e-4 # learning rate of the actor
+LR_CRITIC = 1e-3 # learning rate of the critic
+WEIGHT_DECAY = 0 # L2 weight decay
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+class Agent():
+ """Interacts with and learns from the environment."""
+
+ def __init__(self, state_size, action_size, random_seed):
+ """Initialize an Agent object.
+
+ Params
+ ======
+ state_size (int): dimension of each state
+ action_size (int): dimension of each action
+ random_seed (int): random seed
+ """
+ self.state_size = state_size
+ self.action_size = action_size
+ self.seed = random.seed(random_seed)
+
+ # Actor Network (w/ Target Network)
+ self.actor_local = Actor(state_size, action_size, random_seed).to(device)
+ self.actor_target = Actor(state_size, action_size, random_seed).to(device)
+ self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR)
+
+ # Critic Network (w/ Target Network)
+ self.critic_local = Critic(state_size, action_size, random_seed).to(device)
+ self.critic_target = Critic(state_size, action_size, random_seed).to(device)
+ self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)
+
+ # Noise process
+ self.noise = OUNoise(action_size, random_seed)
+
+ # Replay memory
+ self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
+
+ def step(self, state, action, reward, next_state, done):
+ """Save experience in replay memory, and use random sample from buffer to learn."""
+ # Save experience / reward
+ self.memory.add(state, action, reward, next_state, done)
+
+ # Learn, if enough samples are available in memory
+ if len(self.memory) > BATCH_SIZE:
+ experiences = self.memory.sample()
+ self.learn(experiences, GAMMA)
+
+ def act(self, state, add_noise=True):
+ """Returns actions for given state as per current policy."""
+ state = torch.from_numpy(state).float().to(device)
+ self.actor_local.eval()
+ with torch.no_grad():
+ action = self.actor_local(state).cpu().data.numpy()
+ self.actor_local.train()
+ if add_noise:
+ action += self.noise.sample()
+ return np.clip(action, -1, 1)
+
+ def reset(self):
+ self.noise.reset()
+
+ def learn(self, experiences, gamma):
+ """Update policy and value parameters using given batch of experience tuples.
+ Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
+ where:
+ actor_target(state) -> action
+ critic_target(state, action) -> Q-value
+
+ Params
+ ======
+ experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
+ gamma (float): discount factor
+ """
+ states, actions, rewards, next_states, dones = experiences
+
+ # ---------------------------- update critic ---------------------------- #
+ # Get predicted next-state actions and Q values from target models
+ actions_next = self.actor_target(next_states)
+ Q_targets_next = self.critic_target(next_states, actions_next)
+ # Compute Q targets for current states (y_i)
+ Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
+ # Compute critic loss
+ Q_expected = self.critic_local(states, actions)
+ critic_loss = F.mse_loss(Q_expected, Q_targets)
+ # Minimize the loss
+ self.critic_optimizer.zero_grad()
+ critic_loss.backward()
+ self.critic_optimizer.step()
+
+ # ---------------------------- update actor ---------------------------- #
+ # Compute actor loss
+ actions_pred = self.actor_local(states)
+ actor_loss = -self.critic_local(states, actions_pred).mean()
+ # Minimize the loss
+ self.actor_optimizer.zero_grad()
+ actor_loss.backward()
+ self.actor_optimizer.step()
+
+ # ----------------------- update target networks ----------------------- #
+ self.soft_update(self.critic_local, self.critic_target, TAU)
+ self.soft_update(self.actor_local, self.actor_target, TAU)
+
+ def soft_update(self, local_model, target_model, tau):
+ """Soft update model parameters.
+ θ_target = τ*θ_local + (1 - τ)*θ_target
+
+ Params
+ ======
+ local_model: PyTorch model (weights will be copied from)
+ target_model: PyTorch model (weights will be copied to)
+ tau (float): interpolation parameter
+ """
+ for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
+ target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)
+
+class OUNoise:
+ """Ornstein-Uhlenbeck process."""
+
+ def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):
+ """Initialize parameters and noise process."""
+ self.mu = mu * np.ones(size)
+ self.theta = theta
+ self.sigma = sigma
+ self.seed = random.seed(seed)
+ self.reset()
+
+ def reset(self):
+ """Reset the internal state (= noise) to mean (mu)."""
+ self.state = copy.copy(self.mu)
+
+ def sample(self):
+ """Update internal state and return it as a noise sample."""
+ x = self.state
+ dx = self.theta * (self.mu - x) + self.sigma * np.array([random.random() for i in range(len(x))])
+ self.state = x + dx
+ return self.state
+
+class ReplayBuffer:
+ """Fixed-size buffer to store experience tuples."""
+
+ def __init__(self, action_size, buffer_size, batch_size, seed):
+ """Initialize a ReplayBuffer object.
+ Params
+ ======
+ buffer_size (int): maximum size of buffer
+ batch_size (int): size of each training batch
+ """
+ self.action_size = action_size
+ self.memory = deque(maxlen=buffer_size) # internal memory (deque)
+ self.batch_size = batch_size
+ self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
+ self.seed = random.seed(seed)
+
+ def add(self, state, action, reward, next_state, done):
+ """Add a new experience to memory."""
+ e = self.experience(state, action, reward, next_state, done)
+ self.memory.append(e)
+
+ def sample(self):
+ """Randomly sample a batch of experiences from memory."""
+ experiences = random.sample(self.memory, k=self.batch_size)
+
+ states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
+ actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float().to(device)
+ rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
+ next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)
+ dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)
+
+ return (states, actions, rewards, next_states, dones)
+
+ def __len__(self):
+ """Return the current size of internal memory."""
+ return len(self.memory)
\ No newline at end of file
diff --git a/code/training_udalib/gyroscope_environment_testing.ipynb b/code/training_udalib/gyroscope_environment_testing.ipynb
new file mode 100644
index 0000000..48b647a
--- /dev/null
+++ b/code/training_udalib/gyroscope_environment_testing.ipynb
@@ -0,0 +1,376 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Gyroscope Environment "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "import numpy as np\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self, dt):\n",
+ " \n",
+ " \n",
+ " # Reference angles on theta and phi\n",
+ " self.x1_ref = 0\n",
+ " self.x3_ref = 0\n",
+ " \n",
+ " # Gold disk speed \n",
+ " self.w = 0\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = dt\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 733.04 # rad/s (7000rpm)\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiffAngle = np.pi\n",
+ " self.maxdiskSpeed = 400 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxdiffAngle,self.maxdiffAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, diff_x1, diff_x3, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " reward = diff_x1**2 + diff_x1**2 + .001*(u1**2) + .001*(u2**2)\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " if self.x1_ref is None:\n",
+ " diff_x1 = 0\n",
+ " else:\n",
+ " diff_x1 = angle_normalize(x1-self.x1_ref)\n",
+ " \n",
+ " if self.x3_ref is None:\n",
+ " diff_x3 = 0\n",
+ " else:\n",
+ " diff_x3 = angle_normalize(x3-self.x3_ref)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,diff_x1,diff_x3,w])\n",
+ "\n",
+ " return (self.state, reward, False, {})\n",
+ "\n",
+ " def reset(self, x_0 = None):\n",
+ " \n",
+ " # Generate random state (for training) or take the given one (for simulation)\n",
+ " if x_0 is None:\n",
+ " state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " state = x_0\n",
+ " \n",
+ " \n",
+ " # Reference angles on theta and phi\n",
+ " self.x1_ref = angle_normalize(state[0] - state[4])\n",
+ " self.x3_ref = angle_normalize(state[2] - state[5])\n",
+ " \n",
+ " # Gold disk speed \n",
+ " self.w = state[-1]\n",
+ " \n",
+ " self.state = state\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = gyro.w\n",
+ " dt = gyro.dt\n",
+ " \n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/matthieulc/Documents/MA2/PS1/ps1venv/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Test parameters\n",
+ "w_rpm = 200\n",
+ "w = w_rpm * 2 * np.pi / 60\n",
+ "x_0 = [0.2,0.5,0.3444,0.5,0,0,w]\n",
+ "x1_ref = 1\n",
+ "x3_ref = 1\n",
+ "dt = .02\n",
+ "\n",
+ "# Create env\n",
+ "env = GyroscopeEnv(dt)\n",
+ "env.seed(2)\n",
+ "state = env.reset(x_0)\n",
+ "\n",
+ "# Test\n",
+ "time = np.arange(0, 5.5, dt)\n",
+ "reward = None\n",
+ "val = []\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " u = [0,0]#env.action_space.sample()\n",
+ " state, reward, done, info = env.step([0,0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1368x1368 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(3,2,figsize=(19,19))\n",
+ "time = np.arange(0, 5.5, 0.02)\n",
+ "plt.subplot(3,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[4] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[5] for row in val],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/code/training_udalib/gyroscope_training_udalib.ipynb b/code/training_udalib/gyroscope_training_udalib.ipynb
new file mode 100644
index 0000000..ff2a3c7
--- /dev/null
+++ b/code/training_udalib/gyroscope_training_udalib.ipynb
@@ -0,0 +1,685 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "x83dMPapQBN6"
+ },
+ "source": [
+ "# Gyroscope DDPG training (udacity library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8inmkEG5QHqx"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 655,
+ "status": "ok",
+ "timestamp": 1584030129316,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "DnnJGcnsQvZC",
+ "outputId": "9d6d1408-da29-44ed-af74-4ebdc2bd865a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/Colab Notebooks/DDPG_uda_v0\n"
+ ]
+ }
+ ],
+ "source": [
+ "cd drive/My\\ Drive/Colab Notebooks/DDPG_uda_v0/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "fuJhdd479TpP"
+ },
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'torch'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-d1dc844787f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msolve_ivp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeque\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
+ ]
+ }
+ ],
+ "source": [
+ "import gym\n",
+ "from gym import spaces\n",
+ "from gym.utils import seeding\n",
+ "from os import path\n",
+ "from scipy.integrate import solve_ivp\n",
+ "import random\n",
+ "import torch\n",
+ "import numpy as np\n",
+ "from collections import deque\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "\n",
+ "\n",
+ "\n",
+ "#from vpython import *\n",
+ "from ddpg_agent import Agent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "O0O0t5ZR9Tp6"
+ },
+ "source": [
+ "## Environment Class and Modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Al_k1rtvQBOM"
+ },
+ "outputs": [],
+ "source": [
+ "class GyroscopeEnv(gym.Env):\n",
+ " \n",
+ " \n",
+ " \"\"\"\n",
+ " GyroscopeEnv is a double gimbal control moment gyroscope (DGCMG) with 2 input voltage u1 and u2 \n",
+ " on the two gimbals, and disk speed assumed constant (parameter w). Simulation is based on the \n",
+ " Quanser 3-DOF gyroscope setup.\n",
+ " \n",
+ " \n",
+ " **STATE:**\n",
+ " The state consists of the angle and angular speed of the outer red gimbal (theta = x1, thetadot = x2),\n",
+ " the angle and angular speed of the inner blue gimbal (phi = x3, phidot = x4), the difference to the reference\n",
+ " for tracking on theta and phi (tracking error theta = diff_x1, tracking error phi = diff_x3), and the \n",
+ " disk speed (disk speed = w):\n",
+ " \n",
+ " state = [x1, x2, x3, x4, diff_x1, diff_x3, w]\n",
+ " \n",
+ " **ACTIONS:**\n",
+ " The actions are the input voltage to create the red and blue gimbal torque (red voltage = u1, blue voltage = u2),\n",
+ " and are continuous in a range of -10 and 10V:\n",
+ " \n",
+ " action = [u1,u2]\n",
+ " \n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " metadata = {\n",
+ " 'render.modes' : ['human', 'rgb_array'],\n",
+ " 'video.frames_per_second' : 30\n",
+ " }\n",
+ "\n",
+ " def __init__(self, dt):\n",
+ " \n",
+ " # Inertias in Kg*m2\n",
+ " self.Jbx1 = 0.0019\n",
+ " self.Jbx2 = 0.0008\n",
+ " self.Jbx3 = 0.0012\n",
+ " self.Jrx1 = 0.0179\n",
+ " self.Jdx1 = 0.0028\n",
+ " self.Jdx3 = 0.0056\n",
+ " \n",
+ " # Combined inertias\n",
+ " self.J1 = self.Jbx1 - self.Jbx3 + self.Jdx1 - self.Jdx3\n",
+ " self.J2 = self.Jbx1 + self.Jdx1 + self.Jrx1\n",
+ " self.J3 = self.Jbx2 + self.Jdx1\n",
+ "\n",
+ " # Motor constants\n",
+ " self.Kamp = 0.5 # A/V\n",
+ " self.Ktorque = 0.0704 # Nm/A\n",
+ " self.eff = 0.86\n",
+ " self.nRed = 1.5\n",
+ " self.nBlue = 1\n",
+ " self.KtotRed = self.Kamp*self.Ktorque*self.eff*self.nRed \n",
+ " self.KtotBlue = self.Kamp*self.Ktorque*self.eff*self.nBlue \n",
+ " \n",
+ " # Time step in s\n",
+ " self.dt = dt\n",
+ " \n",
+ " # Action space\n",
+ " self.maxVoltage = 10 # V\n",
+ " self.highAct = np.array([self.maxVoltage,self.maxVoltage])\n",
+ " self.action_space = spaces.Box(low = -self.highAct, high = self.highAct, dtype=np.float32) \n",
+ " \n",
+ " # Observation space (here it is equal to state space)\n",
+ " self.maxSpeed = 40 * 2 * np.pi / 60\n",
+ " self.maxAngle = np.pi\n",
+ " self.maxdiskSpeed = 300 * 2 * np.pi / 60\n",
+ " self.highObs = np.array([self.maxAngle,self.maxSpeed,self.maxAngle,self.maxSpeed,self.maxAngle,self.maxAngle,self.maxdiskSpeed])\n",
+ " self.observation_space = spaces.Box(low = -self.highObs, high = self.highObs, dtype=np.float32)\n",
+ "\n",
+ " # Seed for random number generation\n",
+ " self.seed()\n",
+ " \n",
+ " self.viewer = None\n",
+ "\n",
+ " def seed(self, seed=None):\n",
+ " self.np_random, seed = seeding.np_random(seed)\n",
+ " return [seed]\n",
+ " \n",
+ " \n",
+ "\n",
+ " def step(self,u):\n",
+ " x1, x2, x3, x4, x1_ref, x3_ref, w= self.state \n",
+ " u1,u2 = u\n",
+ " \n",
+ " # Option 1 for reward:\n",
+ " reward = 0.5*(angle_normalize(x1 - x1_ref)**2 + angle_normalize(x3 - x3_ref)**2) #+ .001*(x2**2) + .001*(x4**2) + .001*(u1**2) + .001*(u2**2)\n",
+ "\n",
+ " # Option 2 for reward:\n",
+ " \n",
+ " \"\"\"if diff_x1**2<0.01 and diff_x3**2<0.01:\n",
+ " reward = 0\n",
+ " else:\n",
+ " reward = 1\"\"\"\n",
+ "\n",
+ "\n",
+ " results = solve_ivp(fun = dxdt, t_span = (0, self.dt), y0 = [x1,x2,x3,x4], method='RK45', args=(u1,u2,self))\n",
+ " \n",
+ " x1 = angle_normalize(results.y[0][-1])\n",
+ " x2 = np.clip(results.y[1][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " x3 = angle_normalize(results.y[2][-1])\n",
+ " x4 = np.clip(results.y[3][-1],-self.maxSpeed,self.maxSpeed)\n",
+ " \n",
+ " self.state = np.asarray([x1,x2,x3,x4,x1_ref, x3_ref,w])\n",
+ "\n",
+ " return (self.state, -reward, False, {})\n",
+ "\n",
+ " def reset(self, state = None):\n",
+ " \n",
+ " \n",
+ " # Generate random state (for training) or use given state (for simulation)\n",
+ " if state is None:\n",
+ " self.state = self.np_random.uniform(low=-self.highObs, high=self.highObs)\n",
+ " else:\n",
+ " self.state = state\n",
+ " \n",
+ " return self.state\n",
+ "\n",
+ "\n",
+ " def render(self, mode='human'):\n",
+ " return None\n",
+ " \n",
+ " def close(self):\n",
+ " if self.viewer:\n",
+ " self.viewer.close()\n",
+ " self.viewer = None\n",
+ " \n",
+ "def dxdt(t, x, u1, u2, gyro):\n",
+ " \n",
+ " # Rewrite constants shorter\n",
+ " J1 = gyro.J1\n",
+ " J2 = gyro.J2\n",
+ " J3 = gyro.J3\n",
+ " Jdx3 = gyro.Jdx3\n",
+ " KtotRed = gyro.KtotRed\n",
+ " KtotBlue = gyro.KtotBlue\n",
+ " w = x[-1]\n",
+ "\n",
+ " # Convert input voltage to input torque\n",
+ " u1,u2 = KtotRed*u1, KtotBlue*u2\n",
+ " \n",
+ " # Equations of motion \n",
+ " dx_dt = [0, 0, 0, 0]\n",
+ " dx_dt[0] = x[1]\n",
+ " dx_dt[1] = (u1+J1*np.sin(2*x[2])*x[1]*x[3]-Jdx3*np.cos(x[2])*x[3]*w)/(J2 + J1*np.power(np.sin(x[2]),2))\n",
+ " dx_dt[2] = x[3]\n",
+ " dx_dt[3] = (u2 - J1*np.cos(x[2])*np.sin(x[2])*np.power(x[1],2)+Jdx3*np.cos(x[2])*x[1]*w)/J3\n",
+ " return dx_dt\n",
+ " \n",
+ "def angle_normalize(x):\n",
+ " return (((x+np.pi) % (2*np.pi)) - np.pi) # To keep the angles between -pi and pi\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "2YB2o73h9TrC"
+ },
+ "source": [
+ "## DDPG run"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "uo5HKwz29TrF"
+ },
+ "outputs": [],
+ "source": [
+ "def ddpg(n_episodes=5000, max_t=250, print_every=100):\n",
+ " scores_deque = deque(maxlen=print_every)\n",
+ " scores = []\n",
+ " for i_episode in range(1, n_episodes+1):\n",
+ " state = env.reset(None)\n",
+ " agent.reset()\n",
+ " score = 0\n",
+ " for t in range(max_t):\n",
+ " action = agent.act(state)\n",
+ " next_state, reward, done, _ = env.step(action)\n",
+ " agent.step(state, action, reward, next_state, done)\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ " scores_deque.append(score)\n",
+ " scores.append(score)\n",
+ " print('\\rEpisode {}\\tScore: {:.2f}'.format(i_episode, score), end=\"\")\n",
+ " torch.save(agent.actor_local.state_dict(), 'checkpoint_actor.pth')\n",
+ " torch.save(agent.critic_local.state_dict(), 'checkpoint_critic.pth')\n",
+ " torch.save(agent.actor_optimizer.state_dict(), 'checkpoint_actor_opt.pth')\n",
+ " torch.save(agent.critic_optimizer.state_dict(), 'checkpoint_critic_opt.pth')\n",
+ " if i_episode % print_every == 0:\n",
+ " print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
+ " \n",
+ " return scores\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "be0wYIeBQBOc"
+ },
+ "source": [
+ "## Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 533
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 654004,
+ "status": "error",
+ "timestamp": 1584037207187,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "fLyFHs0yQBOd",
+ "outputId": "260489ff-5e40-416a-e529-5a0cfcaefceb"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n",
+ "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
+ " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Episode 100\tAverage Score: -2418.53\n",
+ "Episode 200\tAverage Score: -1813.93\n",
+ "Episode 300\tAverage Score: -1814.60\n",
+ "Episode 341\tScore: -1640.05"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "ignored",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-53-e407b799b307>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m agent.critic_optimizer.load_state_dict(checkpoint_critic_optimizer)\"\"\"\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mddpg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m<ipython-input-52-dcf8a51d0fa2>\u001b[0m in \u001b[0;36mddpg\u001b[0;34m(n_episodes, max_t, print_every)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/content/drive/My Drive/Colab Notebooks/DDPG_uda_v0/ddpg_agent.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, state, action, reward, next_state, done)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0mexperiences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperiences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGAMMA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_noise\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/content/drive/My Drive/Colab Notebooks/DDPG_uda_v0/ddpg_agent.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, experiences, gamma)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mcritic_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;31m# ---------------------------- update actor ---------------------------- #\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_exp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias_correction2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'eps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias_correction2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'eps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0mstep_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mbias_correction1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "dt = 0.05\n",
+ "env = GyroscopeEnv(dt)\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = Agent(state_size=7, action_size=2, random_seed=2)\n",
+ "\n",
+ "\"\"\"# Access previous checkpoint\n",
+ "checkpoint_actor = torch.load('checkpoint_actor.pth')\n",
+ "checkpoint_critic = torch.load('checkpoint_critic.pth')\n",
+ "checkpoint_actor_optimizer = torch.load('checkpoint_actor_opt.pth')\n",
+ "checkpoint_critic_optimizer = torch.load('checkpoint_critic_opt.pth')\n",
+ "\n",
+ "# Use previous checkpoint\n",
+ "agent.actor_local.load_state_dict(checkpoint_actor)\n",
+ "agent.critic_local.load_state_dict(checkpoint_critic)\n",
+ "agent.actor_optimizer.load_state_dict(checkpoint_actor_optimizer)\n",
+ "agent.critic_optimizer.load_state_dict(checkpoint_critic_optimizer)\"\"\"\n",
+ "\n",
+ "scores = ddpg()\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111)\n",
+ "plt.plot(np.arange(1, len(scores)+1), scores)\n",
+ "plt.ylabel('Score')\n",
+ "plt.xlabel('Episode #')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Gn3Gp40bcOVz"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 972,
+ "status": "ok",
+ "timestamp": 1584036455886,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "6GyY0wE-QBOj",
+ "outputId": "9a5e9011-e024-4a65-8383-83c062cdcad9"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
+ " warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n",
+ "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
+ " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-84.65790332698876\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Creat environment\n",
+ "dt = 0.05\n",
+ "env = GyroscopeEnv(dt)\n",
+ "env.seed(2)\n",
+ "\n",
+ "# Create agent\n",
+ "agent = Agent(state_size=7, action_size=2, random_seed=2)\n",
+ "agent.actor_local.load_state_dict(torch.load('checkpoint_actor.pth'))\n",
+ "agent.critic_local.load_state_dict(torch.load('checkpoint_critic.pth'))\n",
+ "\n",
+ "# Test parameters\n",
+ "x1,x2,x3,x4,x1_ref,x3_ref,w = 2,0,0,0,1,1.22,10\n",
+ "state = env.reset(np.array([x1,x2,x3,x4,x1_ref,x3_ref,w]))\n",
+ "val = []\n",
+ "dt = 0.05\n",
+ "time = np.arange(0, 30, dt)\n",
+ "score = 0\n",
+ "for i in range(len(time)):\n",
+ " val.append(state)\n",
+ " action = agent.act(state, add_noise=False)\n",
+ " state, reward, done, _ = env.step(action)\n",
+ " score += reward\n",
+ " if done:\n",
+ " break \n",
+ "\n",
+ "env.close()\n",
+ "print(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "vvMjuRHDcfrE"
+ },
+ "source": [
+ "## Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "colab_type": "code",
+ "executionInfo": {
+ "elapsed": 1856,
+ "status": "ok",
+ "timestamp": 1584036457424,
+ "user": {
+ "displayName": "Matthieu Le Cauchois",
+ "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgY9gRlHHK-FHlINeRnTJw_wewJsr639GH8MAWl=s64",
+ "userId": "10992927378504656501"
+ },
+ "user_tz": -60
+ },
+ "id": "aCZCqujgcMVA",
+ "outputId": "05490294-aab8-4933-ca9b-e13ba85ecf6d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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zrICRJEnjWlOnIC1fXpIvEycO/1ySJI0RJmCaYfp0K2AkSdK41vQKGNd/kSR1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ACuvnri25IkaToxATMOa9eWZxMwkiRpUHSrBGnBAth1VxMwkqTB0/cETEQcHxHXRsSyiHhrk/ff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\nkiRJkiSNmB0wkiRJkiRJI/Y/r6iEX/XdyloAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 1368x1368 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, axs = plt.subplots(3,2,figsize=(19,19))\n",
+ "plt.subplot(3,2,1)\n",
+ "plt.title('Red gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[0] for row in val],'r-')\n",
+ "plt.plot(time, [row[4] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(3,2,2)\n",
+ "plt.title('Red gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\theta$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[1] for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,3)\n",
+ "plt.title('Blue gimbal angle',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[2] for row in val],'b-')\n",
+ "plt.plot(time, [row[5] for row in val], color='black', linestyle='dashed')\n",
+ "\n",
+ "plt.subplot(3,2,4)\n",
+ "plt.title('Blue gimbal speed',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\dot \\phi$ (rad/s)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[row[3] for row in val],'b-')\n",
+ "\n",
+ "plt.subplot(3,2,5)\n",
+ "plt.title('Red gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\theta$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[(row[0]- row[4]) for row in val],'r-')\n",
+ "\n",
+ "plt.subplot(3,2,6)\n",
+ "plt.title('Blue gimbal tracking error',fontsize=20)\n",
+ "plt.xlabel('time (s)',fontsize=16)\n",
+ "plt.ylabel(r'$\\phi$ error (rad)',fontsize=16)\n",
+ "plt.grid()\n",
+ "plt.plot(time,[(row[2]- row[5]) for row in val],'b-')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fz9r-gaStzpk"
+ },
+ "source": [
+ "## 3D rendering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 0,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "HQM1E8JYc-cC"
+ },
+ "outputs": [],
+ "source": [
+ "# Scene\n",
+ "scene = canvas(background=color.white) \n",
+ "\n",
+ "# Objects\n",
+ "redGimbal = ring(pos=vector(0,0,0), axis=vector(0,0,1), radius=2, thickness=0.2,color=vector(0.9,0,0))\n",
+ "blueGimbal1 = cylinder(pos=vector(0,0,0), axis=vector(0,2,0), radius=0.3,color=color.blue)\n",
+ "blueGimbal2 = cylinder(pos=vector(0,0,0), axis=vector(0,-2,0), radius=0.3,color=color.blue)\n",
+ "disk1 = cylinder(pos=vector(0,0,0), axis=vector(0,0,0.15), radius=1.3,color=color.yellow)\n",
+ "disk2 = cylinder(pos=vector(0,0,0), axis=vector(0,0,-0.15), radius=1.3,color=color.yellow)\n",
+ "baseR = extrusion(path=[vec(0,0,0), vec(0.7,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(2,0,0), color=color.black)\n",
+ "baseL = extrusion(path=[vec(-0.7,0,0), vec(0,0,0)],shape=[ shapes.circle(radius=0.5) ], pos=vec(-2,0,0), color=color.black)\n",
+ "\n",
+ "t = 0\n",
+ "dt = 0.01\n",
+ "loops = 0\n",
+ "ctime = 0\n",
+ "start = clock()\n",
+ "N = 200\n",
+ "\n",
+ "# 6.4/100\n",
+ "for k in range(len(time)):\n",
+ " rate(N)\n",
+ " ct = clock()\n",
+ " theta = val[k][0]\n",
+ " phi = val[k][2]\n",
+ " redGimbal.axis = vector(0,-sin(theta), cos(theta))\n",
+ " blueGimbal1.axis = 2*vector(0,cos(theta), sin(theta))\n",
+ " blueGimbal2.axis = -2*vector(0,cos(theta), sin(theta))\n",
+ " disk1.axis = 0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " disk2.axis = -0.15*vector(-sin(phi),-sin(theta)*cos(phi),cos(theta)*cos(phi))\n",
+ " ctime += clock()-ct\n",
+ " loops += 1"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "gyroscope_ddpg_testing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "ps1venv",
+ "language": "python",
+ "name": "ps1venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/training_udalib/model.py b/code/training_udalib/model.py
new file mode 100644
index 0000000..e979a8b
--- /dev/null
+++ b/code/training_udalib/model.py
@@ -0,0 +1,74 @@
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+def hidden_init(layer):
+ fan_in = layer.weight.data.size()[0]
+ lim = 1. / np.sqrt(fan_in)
+ return (-lim, lim)
+
+class Actor(nn.Module):
+ """Actor (Policy) Model."""
+
+ def __init__(self, state_size, action_size, seed, fc1_units=400, fc2_units=300):
+ """Initialize parameters and build model.
+ Params
+ ======
+ state_size (int): Dimension of each state
+ action_size (int): Dimension of each action
+ seed (int): Random seed
+ fc1_units (int): Number of nodes in first hidden layer
+ fc2_units (int): Number of nodes in second hidden layer
+ """
+ super(Actor, self).__init__()
+ self.seed = torch.manual_seed(seed)
+ self.fc1 = nn.Linear(state_size, fc1_units)
+ self.fc2 = nn.Linear(fc1_units, fc2_units)
+ self.fc3 = nn.Linear(fc2_units, action_size)
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
+ self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
+ self.fc3.weight.data.uniform_(-3e-3, 3e-3)
+
+ def forward(self, state):
+ """Build an actor (policy) network that maps states -> actions."""
+ x = F.relu(self.fc1(state))
+ x = F.relu(self.fc2(x))
+ return F.tanh(self.fc3(x))
+
+
+class Critic(nn.Module):
+ """Critic (Value) Model."""
+
+ def __init__(self, state_size, action_size, seed, fcs1_units=400, fc2_units=300):
+ """Initialize parameters and build model.
+ Params
+ ======
+ state_size (int): Dimension of each state
+ action_size (int): Dimension of each action
+ seed (int): Random seed
+ fcs1_units (int): Number of nodes in the first hidden layer
+ fc2_units (int): Number of nodes in the second hidden layer
+ """
+ super(Critic, self).__init__()
+ self.seed = torch.manual_seed(seed)
+ self.fcs1 = nn.Linear(state_size, fcs1_units)
+ self.fc2 = nn.Linear(fcs1_units+action_size, fc2_units)
+ self.fc3 = nn.Linear(fc2_units, 1)
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))
+ self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
+ self.fc3.weight.data.uniform_(-3e-3, 3e-3)
+
+ def forward(self, state, action):
+ """Build a critic (value) network that maps (state, action) pairs -> Q-values."""
+ xs = F.relu(self.fcs1(state))
+ x = torch.cat((xs, action), dim=1)
+ x = F.relu(self.fc2(x))
+ return self.fc3(x)
diff --git a/code/training_udalib/workingNN_sserror/checkpoint_actor.pth b/code/training_udalib/workingNN_sserror/checkpoint_actor.pth
new file mode 100644
index 0000000..f12a48a
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diff --git a/code/training_udalib/workingNN_sserror/checkpoint_critic.pth b/code/training_udalib/workingNN_sserror/checkpoint_critic.pth
new file mode 100644
index 0000000..f39221c
Binary files /dev/null and b/code/training_udalib/workingNN_sserror/checkpoint_critic.pth differ
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