diff --git a/montecarlo.py b/montecarlo.py
new file mode 100644
index 0000000..0a03398
--- /dev/null
+++ b/montecarlo.py
@@ -0,0 +1,83 @@
+import random
+import pandas as pd
+import numpy as np
+
+
+class MonteCarlo(object):
+ def __init__(self, ini_q=0.0, epsilon=0.1, alpha=0.2, gamma=0.9):
+ self.q = self.initQ(ini_q)
+ self.epsilon = epsilon
+ self.alpha = alpha
+ self.gamma = gamma
+ self.state_actions_this_episode = []
+
+
+ def getQ(self, state, action):
+ state = tuple(state)
+ action = tuple(action)
+ return self.q[action][state]
+
+ def setQ(self, state, action, val):
+ state = tuple(state)
+ action = tuple(action)
+ self.q[action][state] = val
+
+ def initQ(self, ini_q, max_obj=7):
+ actions=[]
+ for i in range(1,4):
+ for j in range(1,max_obj+1):
+ actions.append((i,j))
+
+ self.actions = actions
+
+ q = pd.DataFrame(columns=actions, dtype=np.float64)
+
+ for i in range(0,max_obj+1):
+ for j in range(0,max_obj+1):
+ for k in range(0,max_obj+1):
+ q = q.append(pd.Series([ini_q]*len(actions),
+ index=q.columns,
+ name=(i,j,k)))
+
+ return q
+
+
+ def decide(self, state):
+ elig_actions = [a for a in self.actions if state[a[0]-1] >= a[1]]
+ if random.random() < self.epsilon:
+ action = random.choice(elig_actions)
+ else:
+ q = [self.getQ(state, a) for a in self.actions if state[a[0]-1] >= a[1]]
+ maxQ = max(q)
+ count = q.count(maxQ)
+ if count > 1:
+ best = [i for i in range(len(elig_actions)) if q[i] == maxQ]
+ i = random.choice(best)
+ else:
+ i = q.index(maxQ)
+ action = elig_actions[i]
+ return list(action)
+
+ def exploit(self, state):
+ elig_actions = [a for a in self.actions if state[a[0]-1] >= a[1]]
+ q = [self.getQ(state, a) for a in self.actions if state[a[0]-1] >= a[1]]
+ maxQ = max(q)
+ count = q.count(maxQ)
+ if count > 1:
+ best = [i for i in range(len(elig_actions)) if q[i] == maxQ]
+ i = random.choice(best)
+ else:
+ i = q.index(maxQ)
+ action = elig_actions[i]
+ return list(action)
+
+ def append_state_actions(self, state, action):
+ self.state_actions_this_episode.append((state, action))
+
+ def learn(self, reward):
+ for i, (state, action) in enumerate(reversed(self.state_actions_this_episode)):
+ newv = reward / (i+1)
+ self.setQ(state, action, newv)
+ reward *= self.gamma
+
+ self.state_actions_this_episode = []
diff --git a/nim-game.ipynb b/nim-game.ipynb
index 0446b05..d2f362c 100644
--- a/nim-game.ipynb
+++ b/nim-game.ipynb
@@ -1,1284 +1,1294 @@
{
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from nim_env import NimEnv\n",
"from utils import compute_nim_sum, optimal_policy, random_policy\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"# from agent import RLAgent\n",
"from sarsa import RLAgent\n",
"from tqdm import tqdm\n",
"import pandas as pd\n",
"pd.options.display.float_format = '{:,.3f}'.format\n",
"\n",
"# plt figure setup\n",
"from matplotlib import rc\n",
"\n",
"plt.rc('axes', labelsize=14) # fontsize of the x and y labels\n",
"plt.rc('axes', titlesize=14)\n",
"plt.rc('xtick', labelsize=13) # fontsize of the tick labels\n",
"plt.rc('ytick', labelsize=13)\n",
"plt.rc('legend', fontsize=14) # legend fontsize\n",
"plt.rc('figure', titlesize=14) # fontsize of the figure title\n",
"plt.rc('lines', markersize=7)\n",
"plt.rc('lines', linewidth=2)\n",
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SARSA agent against fixed policy (teacher)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"\n",
"def evaluate_fixed(agent, N):\n",
" player0_ep_rewards = [0]*N\n",
" player1_ep_rewards = [0]*N\n",
" for i in range(N):\n",
" done = False\n",
" heaps = random.sample(range(1, 8), 3)\n",
" env = NimEnv(heaps)\n",
" turn = 0\n",
" while not done:\n",
" action = agent.decide(heaps)\n",
" next_heaps, winner, reward, done, turn = env.step(action)\n",
" if done:\n",
" break\n",
" adv_action = optimal_policy(next_heaps, randomness=0.2)\n",
" nextnext_heaps, winner, adv_reward, done, _ = env.step(adv_action)\n",
" heaps = nextnext_heaps\n",
"\n",
"\n",
" if winner == 0:\n",
" player0_ep_rewards.append(1) \n",
" player1_ep_rewards.append(-1) \n",
" else: # if winner == 1 \n",
" player0_ep_rewards.append(-1)\n",
" player1_ep_rewards.append(1) \n",
"\n",
" return np.mean(player0_ep_rewards), np.mean(player1_ep_rewards)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"heaps = [7,7,7]\n",
"env = NimEnv(heaps)\n",
"\n",
"\n",
"def run_experiment(env, num_episodes):\n",
" player0_rewards = []\n",
" player1_rewards = []\n",
" agent0 = RLAgent() # RL agent for player 0\n",
" # agent1 = RLAgent() # RL agent for player 1\n",
"\n",
" for episode in tqdm(range(1, num_episodes+1)):\n",
" heaps = env.reset() \n",
" env = NimEnv(heaps)\n",
" \n",
" winner = None\n",
" done = False\n",
"\n",
" action = None\n",
" reward = None\n",
" old_0 = (None, None) # old state/action for player0\n",
" old_reward = None\n",
" i = 0\n",
" while not done:\n",
" action = agent0.decide(heaps)\n",
" next_heaps, winner, reward, done, _ = env.step(action)\n",
" if i > 0:\n",
" agent0.learn(old_0[0], old_0[1], old_reward, heaps, action)\n",
" if done:\n",
" agent0.learn(heaps, action, reward[0])\n",
" break\n",
" \n",
" adv_action = optimal_policy(next_heaps, randomness=0.2)\n",
" nextnext_heaps, winner, adv_reward, done, _ = env.step(adv_action)\n",
" if done:\n",
" agent0.learn(heaps, action, adv_reward[0])\n",
" break\n",
" \n",
" old_0 = heaps, action\n",
" old_reward = adv_reward[0]\n",
" heaps = nextnext_heaps\n",
" i += 1\n",
"\n",
" r0, r1 = evaluate_fixed(agent0, 10)\n",
" player0_rewards.append(r0) \n",
" player1_rewards.append(r1) \n",
" \n",
" return player0_rewards, player1_rewards, agent0"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 50000/50000 [03:20<00:00, 249.48it/s]\n"
]
}
],
"source": [
"num_episodes = 50000\n",
"r0, r1, agent = run_experiment(env, num_episodes)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N=100\n",
"r0_conv = np.convolve(r0, np.ones(N)/N, mode='valid')\n",
"r1_conv = np.convolve(r1, np.ones(N)/N, mode='valid')\n",
"fig = plt.figure(figsize=(12,8))\n",
"plt.plot(range(1, len(r0_conv)+1), r0_conv, color='b', label='SARSA (player 0)')\n",
"plt.plot(range(1, len(r1_conv)+1), r1_conv, color='r', label=r'Optimal ($\\epsilon = 0.2$)')\n",
"plt.legend()\n",
"plt.xlabel('episode')\n",
"plt.ylabel('reward')\n",
"plt.grid(True,'major',linestyle='-',linewidth=0.5)\n",
"plt.grid(True,'minor',linestyle='--',linewidth=0.25) \n",
"plt.title(r'SARSA against $\\epsilon$-greedy optimal policy')\n",
"plt.show()"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q-table\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" (1, 1) | \n",
" (1, 2) | \n",
" (1, 3) | \n",
" (1, 4) | \n",
" (1, 5) | \n",
" (1, 6) | \n",
" (1, 7) | \n",
" (2, 1) | \n",
" (2, 2) | \n",
" (2, 3) | \n",
" ... | \n",
" (2, 5) | \n",
" (2, 6) | \n",
" (2, 7) | \n",
" (3, 1) | \n",
" (3, 2) | \n",
" (3, 3) | \n",
" (3, 4) | \n",
" (3, 5) | \n",
" (3, 6) | \n",
" (3, 7) | \n",
"
\n",
" \n",
" \n",
" \n",
" (0, 0, 0) | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " 0.889 | \n",
+ " 0.741 | \n",
+ " 0.794 | \n",
+ " 0.628 | \n",
+ " 0.820 | \n",
+ " 0.794 | \n",
+ " 0.837 | \n",
+ " 0.898 | \n",
+ " 0.445 | \n",
+ " 0.824 | \n",
" ... | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " 0.693 | \n",
+ " 0.724 | \n",
+ " 0.650 | \n",
+ " 0.859 | \n",
+ " 0.585 | \n",
+ " 0.488 | \n",
+ " 0.537 | \n",
+ " 0.432 | \n",
+ " 0.766 | \n",
+ " 0.793 | \n",
"
\n",
" \n",
" (0, 0, 1) | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.811 | \n",
+ " -0.866 | \n",
+ " -0.852 | \n",
+ " -0.815 | \n",
+ " -0.861 | \n",
+ " -0.868 | \n",
+ " -0.853 | \n",
+ " -0.818 | \n",
+ " -0.846 | \n",
+ " -0.787 | \n",
" ... | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.819 | \n",
+ " -0.820 | \n",
+ " -0.877 | \n",
+ " 1.000 | \n",
+ " -0.875 | \n",
+ " -0.824 | \n",
+ " -0.851 | \n",
+ " -0.891 | \n",
+ " -0.886 | \n",
" 0.000 | \n",
"
\n",
" \n",
" (0, 0, 2) | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.458 | \n",
+ " -0.400 | \n",
+ " -0.513 | \n",
+ " -0.474 | \n",
+ " -0.512 | \n",
+ " -0.468 | \n",
+ " -0.571 | \n",
+ " -0.442 | \n",
+ " -0.462 | \n",
+ " -0.510 | \n",
" ... | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.605 | \n",
+ " -0.520 | \n",
+ " -0.438 | \n",
+ " -0.675 | \n",
+ " 1.000 | \n",
+ " -0.440 | \n",
+ " -0.485 | \n",
+ " -0.475 | \n",
" 0.000 | \n",
" 0.000 | \n",
"
\n",
" \n",
" (0, 0, 3) | \n",
+ " -0.467 | \n",
+ " -0.486 | \n",
" 0.000 | \n",
+ " -0.294 | \n",
+ " -0.403 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.110 | \n",
+ " -0.469 | \n",
+ " -0.226 | \n",
+ " -0.166 | \n",
" ... | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.276 | \n",
+ " -0.161 | \n",
+ " -0.239 | \n",
+ " -0.879 | \n",
+ " -0.915 | \n",
+ " 1.000 | \n",
+ " -0.092 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
"
\n",
" \n",
" (0, 0, 4) | \n",
+ " -0.466 | \n",
+ " -0.265 | \n",
" 0.000 | \n",
" 0.000 | \n",
+ " -0.337 | \n",
" 0.000 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.462 | \n",
+ " -0.188 | \n",
+ " -0.036 | \n",
" ... | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.184 | \n",
+ " -0.176 | \n",
+ " -0.129 | \n",
+ " -0.620 | \n",
+ " -0.824 | \n",
+ " -0.768 | \n",
+ " 1.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
"
\n",
" \n",
" (0, 0, 5) | \n",
+ " -0.090 | \n",
" 0.000 | \n",
" 0.000 | \n",
+ " -0.107 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.143 | \n",
+ " -0.044 | \n",
" 0.000 | \n",
" ... | \n",
" 0.000 | \n",
+ " -0.036 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.369 | \n",
+ " -0.482 | \n",
+ " -0.589 | \n",
+ " -0.200 | \n",
+ " 1.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
"
\n",
" \n",
" (0, 0, 6) | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
+ " -0.036 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
+ " -0.179 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.059 | \n",
" ... | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
+ " -0.226 | \n",
+ " -0.200 | \n",
+ " -0.360 | \n",
+ " -0.200 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " 0.999 | \n",
" 0.000 | \n",
"
\n",
" \n",
" (0, 0, 7) | \n",
" 0.000 | \n",
+ " -0.011 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " 0.006 | \n",
+ " -0.036 | \n",
" ... | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.360 | \n",
+ " -0.200 | \n",
+ " -0.200 | \n",
+ " -0.360 | \n",
+ " 0.965 | \n",
"
\n",
" \n",
" (0, 1, 0) | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.734 | \n",
+ " -0.827 | \n",
+ " -0.828 | \n",
+ " -0.853 | \n",
+ " -0.861 | \n",
+ " -0.887 | \n",
+ " -0.888 | \n",
" 1.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.712 | \n",
+ " -0.883 | \n",
" ... | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.847 | \n",
+ " -0.882 | \n",
+ " 0.000 | \n",
+ " -0.766 | \n",
+ " -0.857 | \n",
+ " -0.873 | \n",
+ " -0.864 | \n",
+ " -0.856 | \n",
+ " -0.859 | \n",
+ " -0.868 | \n",
"
\n",
" \n",
" (0, 1, 1) | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " 0.800 | \n",
+ " 0.801 | \n",
+ " 0.798 | \n",
+ " 0.803 | \n",
+ " 0.788 | \n",
+ " 0.785 | \n",
+ " 0.637 | \n",
+ " -0.992 | \n",
+ " 0.755 | \n",
+ " 0.767 | \n",
" ... | \n",
+ " 0.763 | \n",
+ " 0.780 | \n",
" 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
- " 0.000 | \n",
+ " -0.994 | \n",
+ " 0.798 | \n",
+ " 0.745 | \n",
+ " 0.797 | \n",
+ " 0.698 | \n",
+ " 0.769 | \n",
" 0.000 | \n",
"
\n",
" \n",
"
\n",
"
10 rows × 21 columns
\n",
"
"
],
"text/plain": [
" (1, 1) (1, 2) (1, 3) (1, 4) (1, 5) (1, 6) (1, 7) (2, 1) \\\n",
- "(0, 0, 0) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 1) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 2) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 3) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 4) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 5) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 6) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 7) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 1, 0) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 \n",
- "(0, 1, 1) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n",
+ "(0, 0, 0) 0.889 0.741 0.794 0.628 0.820 0.794 0.837 0.898 \n",
+ "(0, 0, 1) -0.811 -0.866 -0.852 -0.815 -0.861 -0.868 -0.853 -0.818 \n",
+ "(0, 0, 2) -0.458 -0.400 -0.513 -0.474 -0.512 -0.468 -0.571 -0.442 \n",
+ "(0, 0, 3) -0.467 -0.486 0.000 -0.294 -0.403 0.000 -0.110 -0.469 \n",
+ "(0, 0, 4) -0.466 -0.265 0.000 0.000 -0.337 0.000 0.000 -0.462 \n",
+ "(0, 0, 5) -0.090 0.000 0.000 -0.107 0.000 0.000 0.000 -0.143 \n",
+ "(0, 0, 6) 0.000 0.000 0.000 -0.036 0.000 0.000 0.000 -0.179 \n",
+ "(0, 0, 7) 0.000 -0.011 0.000 0.000 0.000 0.000 0.000 0.000 \n",
+ "(0, 1, 0) -0.734 -0.827 -0.828 -0.853 -0.861 -0.887 -0.888 1.000 \n",
+ "(0, 1, 1) 0.800 0.801 0.798 0.803 0.788 0.785 0.637 -0.992 \n",
"\n",
" (2, 2) (2, 3) ... (2, 5) (2, 6) (2, 7) (3, 1) (3, 2) \\\n",
- "(0, 0, 0) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 1) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 2) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 3) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 4) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 5) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 6) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 7) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 1, 0) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 1, 1) 0.000 0.000 ... 0.000 0.000 0.000 0.000 0.000 \n",
+ "(0, 0, 0) 0.445 0.824 ... 0.693 0.724 0.650 0.859 0.585 \n",
+ "(0, 0, 1) -0.846 -0.787 ... -0.819 -0.820 -0.877 1.000 -0.875 \n",
+ "(0, 0, 2) -0.462 -0.510 ... -0.605 -0.520 -0.438 -0.675 1.000 \n",
+ "(0, 0, 3) -0.226 -0.166 ... -0.276 -0.161 -0.239 -0.879 -0.915 \n",
+ "(0, 0, 4) -0.188 -0.036 ... -0.184 -0.176 -0.129 -0.620 -0.824 \n",
+ "(0, 0, 5) -0.044 0.000 ... 0.000 -0.036 0.000 -0.369 -0.482 \n",
+ "(0, 0, 6) 0.000 -0.059 ... 0.000 0.000 0.000 -0.226 -0.200 \n",
+ "(0, 0, 7) 0.006 -0.036 ... 0.000 0.000 0.000 0.000 0.000 \n",
+ "(0, 1, 0) -0.712 -0.883 ... -0.847 -0.882 0.000 -0.766 -0.857 \n",
+ "(0, 1, 1) 0.755 0.767 ... 0.763 0.780 0.000 -0.994 0.798 \n",
"\n",
" (3, 3) (3, 4) (3, 5) (3, 6) (3, 7) \n",
- "(0, 0, 0) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 1) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 2) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 3) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 4) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 5) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 6) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 0, 7) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 1, 0) 0.000 0.000 0.000 0.000 0.000 \n",
- "(0, 1, 1) 0.000 0.000 0.000 0.000 0.000 \n",
+ "(0, 0, 0) 0.488 0.537 0.432 0.766 0.793 \n",
+ "(0, 0, 1) -0.824 -0.851 -0.891 -0.886 0.000 \n",
+ "(0, 0, 2) -0.440 -0.485 -0.475 0.000 0.000 \n",
+ "(0, 0, 3) 1.000 -0.092 0.000 0.000 0.000 \n",
+ "(0, 0, 4) -0.768 1.000 0.000 0.000 0.000 \n",
+ "(0, 0, 5) -0.589 -0.200 1.000 0.000 0.000 \n",
+ "(0, 0, 6) -0.360 -0.200 0.000 0.999 0.000 \n",
+ "(0, 0, 7) -0.360 -0.200 -0.200 -0.360 0.965 \n",
+ "(0, 1, 0) -0.873 -0.864 -0.856 -0.859 -0.868 \n",
+ "(0, 1, 1) 0.745 0.797 0.698 0.769 0.000 \n",
"\n",
"[10 rows x 21 columns]"
]
},
- "execution_count": 7,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Q-table')\n",
- "agent[1].q.head(10)"
+ "agent.q.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SARSA agent against another SARSA agent"
]
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"\n",
"def evaluate(agent, N):\n",
" player0_ep_rewards = [0]*N\n",
" player1_ep_rewards = [0]*N\n",
" for i in range(N):\n",
" done = False\n",
" heaps = random.sample(range(1, 8), 3)\n",
" env = NimEnv(heaps)\n",
" turn = 0\n",
" while not done:\n",
" action = agent[turn].decide(heaps)\n",
" next_heaps, winner, reward, done, turn = env.step(action)\n",
" if winner == 0:\n",
" player0_ep_rewards.append(1) \n",
" player1_ep_rewards.append(-1) \n",
" else: # if winner == 1 \n",
" player0_ep_rewards.append(-1)\n",
" player1_ep_rewards.append(1) \n",
"\n",
" return np.mean(player0_ep_rewards), np.mean(player1_ep_rewards)\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"heaps = [7,7,7]\n",
"env = NimEnv(heaps)\n",
"\n",
"\n",
"def run_experiment(env, num_episodes):\n",
" player0_rewards = []\n",
" player1_rewards = []\n",
" agent0 = RLAgent() # RL agent for player 0\n",
" agent1 = RLAgent() # RL agent for player 1\n",
"\n",
" for episode in tqdm(range(1, num_episodes+1)):\n",
" heaps = env.reset() \n",
" env = NimEnv(heaps)\n",
" \n",
" winner = None\n",
" done = False\n",
"\n",
" action = [None, None]\n",
" reward = [None, None]\n",
" old_0 = (None, None) # old state/action for player0\n",
" old_1 = (None, None) # old state/action for player1\n",
" old_reward = None\n",
" i = 0\n",
" while not done:\n",
" action[0] = agent0.decide(heaps)\n",
" next_heaps, winner, reward[0], done, _ = env.step(action[0])\n",
" if i > 0:\n",
" agent0.learn(old_0[0], old_0[1], old_reward[0], heaps, action[0])\n",
" if done:\n",
" agent0.learn(heaps, action[0], reward[0][0])\n",
" if i > 0:\n",
" agent1.learn(old_1[0], old_1[1], reward[0][1])\n",
" break\n",
" \n",
" action[1] = agent1.decide(next_heaps)\n",
" nextnext_heaps, winner, reward[1], done, _ = env.step(action[1])\n",
" if i > 0:\n",
" agent1.learn(old_1[0], old_1[1], old_reward[1], next_heaps, action[1])\n",
" if done:\n",
" agent0.learn(heaps, action[0], reward[1][0])\n",
" agent1.learn(next_heaps, action[1], reward[1][1])\n",
" break\n",
" \n",
"\n",
" old_0 = heaps, action[0]\n",
" old_1 = next_heaps, action[1]\n",
" old_reward = reward[1]\n",
" heaps = nextnext_heaps\n",
" i += 1\n",
"\n",
" r0, r1 = evaluate([agent0, agent1], 10)\n",
" player0_rewards.append(r0) \n",
" player1_rewards.append(r1) \n",
" \n",
" return player0_rewards, player1_rewards, [agent0, agent1]\n"
]
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 50000/50000 [05:02<00:00, 165.17it/s]\n"
+ "100%|██████████| 100000/100000 [10:33<00:00, 157.84it/s]\n"
]
}
],
"source": [
- "num_episodes = 50000\n",
+ "num_episodes = 100_000\n",
"r0, r1, agents = run_experiment(env, num_episodes)"
]
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
- "N=100\n",
+ "N=500\n",
"r0_conv = np.convolve(r0, np.ones(N)/N, mode='valid')\n",
"r1_conv = np.convolve(r1, np.ones(N)/N, mode='valid')\n",
"fig = plt.figure(figsize=(12,8))\n",
"plt.plot(range(1, len(r0_conv)+1), r0_conv, color='b', label='player0')\n",
"plt.plot(range(1, len(r1_conv)+1), r1_conv, color='r', label='player1')\n",
"plt.legend()\n",
"plt.xlabel('episode')\n",
"plt.ylabel('reward')\n",
"plt.grid(True,'major',linestyle='-',linewidth=0.5)\n",
"plt.grid(True,'minor',linestyle='--',linewidth=0.25) \n",
"plt.title(r'SARSA against SARSA (first to play advantage?)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q-table\n"
]
},
{
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"[10 rows x 21 columns]"
]
},
- "execution_count": 9,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Q-table')\n",
"agents[1].q.head(10)"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The environment at beginning is: [6, 4, 2]\n",
- "agent took action\n",
- "[6, 4, 1]\n",
- "you took action\n",
- "[2, 4, 1]\n",
- "agent took action\n",
- "[2, 3, 1]\n",
- "you took action\n",
- "[2, 2, 1]\n",
- "agent took action\n",
- "[2, 2, 0]\n",
- "you took action\n",
- "[2, 0, 0]\n",
- "agent took action\n",
+ "The environment at beginning is: [3, 1, 5]\n",
+ "agent took action [3, 3]\n",
+ "[3, 1, 2]\n",
+ "you took action [1, 1]\n",
+ "[2, 1, 2]\n",
+ "agent took action [2, 1]\n",
+ "[2, 0, 2]\n",
+ "you took action [1, 1]\n",
+ "[1, 0, 2]\n",
+ "agent took action [3, 1]\n",
+ "[1, 0, 1]\n",
+ "you took action [3, 1]\n",
+ "[1, 0, 0]\n",
+ "agent took action [1, 1]\n",
"[0, 0, 0]\n",
"agent win(s)!\n"
]
}
],
"source": [
"heaps = random.sample(range(1, 8), 3)\n",
"env = NimEnv(heaps)\n",
"agent = agents[1]\n",
"print('The environment at beginning is:', end=' ')\n",
"env.render(simple=True)\n",
"done = False\n",
"\n",
"while not done:\n",
" action = agent.exploit(heaps)\n",
" heaps, winner, reward, done, turn = env.step(action)\n",
- " print('agent took action')\n",
+ " print('agent took action ', action)\n",
" env.render(simple=True)\n",
" if done:\n",
" break\n",
" entry = input('enter [heap, n_objects]: ')\n",
" move = [int(entry[0]), int(entry[2])]\n",
" heaps, winner, reward, done, turn = env.step(move)\n",
- " print('you took action')\n",
+ " print('you took action ', move)\n",
" env.render(simple=True)\n",
" # print('\\n')\n",
" # print('\\n')\n",
" # print(f\"player {turn['next_turn']} turn\")\n",
" \n",
"\n",
"# print('\\nHere is the reward: ', reward)\n",
"winner = 'agent' if winner == 0 else 'you'\n",
"print(f'{winner} win(s)!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Old"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_episodes = 500\n",
"num_seed = 10\n",
"heaps = random.sample(range(1, 8), 3)\n",
"env = NimEnv(heaps)\n",
"\n",
"\n",
"def run_experiment(env, num_episodes):\n",
" player0_rewards = []\n",
" player1_rewards = []\n",
" agent = [RLAgent(), RLAgent()] # agents for player 0,1\n",
" # agent = RLAgent() # player 0 plays against e-greedy optimal policy\n",
" # agent0 = RLAgent() # RL agent for player 0\n",
" # agent1 = RLAgent() # RL agent for player 1\n",
" actions = [None, None]\n",
" reward = [None, None]\n",
"\n",
" for episode in tqdm(range(1, num_episodes+1)):\n",
" player0_episode_reward = []\n",
" player1_episode_reward = []\n",
"\n",
" for seed in range(num_seed):\n",
" heaps = env.reset(seed) # fixed seeds to evaluate performance on same set of initial conditions\n",
" env = NimEnv(heaps)\n",
" winner = None\n",
" done = False\n",
" old_heaps = None\n",
" turn = 0\n",
" next_turn = None\n",
" action[0] = agent[0].decide(heaps)\n",
" while not done:\n",
" # print(heaps)\n",
" # print(action)\n",
" next_heaps = None\n",
" nextnext_heaps = None\n",
" next_heaps, winner, reward0, done, next_turn = env.step(action0) # agent0 takes action a\n",
" if done:\n",
" agent[turn].learn(heaps, action[turn], reward[turn][0])\n",
" if old_heaps is not None:\n",
" agent[next_turn].learn(old_heaps, action1, reward[turn][1])\n",
" break\n",
" action1 = agent1.decide(next_heaps)\n",
" nextnext_heaps, winner, reward1, done, _ = env.step(action1) # agent1 takes action a'\n",
" if done:\n",
" agent0.learn(heaps, action0, reward1[0])\n",
" agent1.learn(next_heaps, action1, reward1[1])\n",
" break\n",
" next_action0 = agent0.decide(nextnext_heaps)\n",
" next3_heaps, winner, reward0, done, _ = env.step(next_action0)\n",
" agent0.learn(heaps, action0, reward0[0], nextnext_heaps, next_action0)\n",
" if done:\n",
" agent1.learn(next_heaps, action1, reward0[1])\n",
" break\n",
" next_action1 = agent1.decide(next3_heaps)\n",
" agent1.learn(next_heaps, action1, reward1[1], next3_heaps, next_action1)\n",
" next4_heaps, winner, reward1, done, _ = env.step(next_action1)\n",
" if done:\n",
" agent0.learn(nextnext_heaps, next_action0, reward1[0])\n",
" agent1.learn(next3_heaps, next_action1, reward1[1])\n",
" break\n",
" nextnext_action0 = agent0.decide(next4_heaps)\n",
"\n",
" old_heaps = next3_heaps.copy()\n",
" action1 = next_action1.copy()\n",
" heaps = next4_heaps.copy()\n",
" action0 = nextnext_action0.copy()\n",
"\n",
" if winner == 0:\n",
" player0_episode_reward.append(1) \n",
" player1_episode_reward.append(-1) \n",
" else: # if winner == 1 \n",
" player0_episode_reward.append(-1)\n",
" player1_episode_reward.append(1) \n",
" \n",
" player0_rewards.append(np.mean(player0_episode_reward)) \n",
" player1_rewards.append(np.mean(player1_episode_reward)) \n",
" return player0_rewards, player1_rewards, agent\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"num_episodes = 5000\n",
"num_seed = 100\n",
"heaps = random.sample(range(1, 8), 3)\n",
"env = NimEnv(heaps)\n",
"\n",
"\n",
"def run_experiment(env, num_episodes):\n",
" player0_rewards = []\n",
" player1_rewards = []\n",
" # agent = [RLAgent(), RLAgent()] # agents for player 0,1\n",
" agent = RLAgent() # player 0 plays against e-greedy optimal policy\n",
" # agent0 = RLAgent() # RL agent for player 0\n",
" # agent1 = RLAgent() # RL agent for player 1\n",
"\n",
" for episode in tqdm(range(1, num_episodes+1)):\n",
" player0_episode_reward = []\n",
" player1_episode_reward = []\n",
"\n",
" for seed in range(num_seed):\n",
" heaps = env.reset(seed) # fixed seeds to evaluate performance on same set of initial conditions\n",
" #heaps = [7,7,7]\n",
" env = NimEnv(heaps)\n",
" winner = None\n",
" done = False\n",
" action = agent.decide(heaps)\n",
" while not done:\n",
" # print(heaps)\n",
" # print(action)\n",
" next_heaps = None\n",
" nextnext_heaps = None\n",
" next_heaps, winner, rewards, done, _ = env.step(action)\n",
" if done:\n",
" agent.learn(heaps, action, rewards[0])\n",
" break\n",
" adv_action = optimal_policy(next_heaps, randomness=0.2)\n",
" # print('nextheaps1=', next_heaps)\n",
" # print('adv_action=', adv_action)\n",
" nextnext_heaps, winner, adv_reward, done, _ = env.step(adv_action)\n",
" if done:\n",
" agent.learn(heaps, action, adv_reward[0]) #adv_reward[0]\n",
" break\n",
" nextnext_action = agent.decide(nextnext_heaps)\n",
" # print(f'Q({heaps}, {action}) = ', agent.getQ(heaps, action))\n",
" agent.learn(heaps, action, rewards[0], nextnext_heaps, nextnext_action)\n",
" # print('Q(heaps, action) = ', agent.getQ(heaps, action))\n",
"\n",
" heaps = nextnext_heaps.copy()\n",
" action = nextnext_action.copy()\n",
"\n",
" if winner == 0:\n",
" player0_episode_reward.append(1) \n",
" player1_episode_reward.append(-1) \n",
" else: # if winner == 1 \n",
" player0_episode_reward.append(-1)\n",
" player1_episode_reward.append(1) \n",
" \n",
" player0_rewards.append(np.mean(player0_episode_reward)) \n",
" player1_rewards.append(np.mean(player1_episode_reward)) \n",
" return player0_rewards, player1_rewards, agent\n"
]
}
],
"metadata": {
"interpreter": {
"hash": "4369559244255f10d34bca352df9b3f8794934e60b17f3451fa3b0f2f96527a8"
},
"kernelspec": {
"display_name": "Python 3.8.8 64-bit ('base': conda)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"name": "python",
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}
diff --git a/sarsa.png b/sarsa.png
new file mode 100644
index 0000000..7d478bb
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diff --git a/sarsa_elig.png b/sarsa_elig.png
new file mode 100644
index 0000000..84ec79e
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diff --git a/sarsa_lambda.py b/sarsa_lambda.py
new file mode 100644
index 0000000..2a4697b
--- /dev/null
+++ b/sarsa_lambda.py
@@ -0,0 +1,119 @@
+import random
+import pandas as pd
+import numpy as np
+
+
+class SARSA_LAMBDA(object):
+ def __init__(self, ini_q=0.0, epsilon=0.1, alpha=0.2, gamma=0.9, lambda_=0.9):
+ self.q = self.initQ(ini_q)
+ self.e = self.initE(0.0)
+ self.epsilon = epsilon
+ self.alpha = alpha
+ self.gamma = gamma
+ self.lambda_ = lambda_
+
+ def resetE(self):
+ self.e = 0.0*self.q
+
+ def getQ(self, state, action):
+ state = tuple(state)
+ action = tuple(action)
+ return self.q[action][state]
+
+ def setQ(self, state, action, val):
+ state = tuple(state)
+ action = tuple(action)
+ self.e[action][state] = val
+
+ def getE(self, state, action):
+ state = tuple(state)
+ action = tuple(action)
+ return self.e[action][state]
+
+ def setE(self, state, action, val):
+ state = tuple(state)
+ action = tuple(action)
+ self.e[action][state] = val
+
+ def initQ(self, ini_q, max_obj=7):
+ actions=[]
+ for i in range(1,4):
+ for j in range(1,max_obj+1):
+ actions.append((i,j))
+
+ self.actions = actions
+
+ q = pd.DataFrame(columns=actions, dtype=np.float64)
+
+ for i in range(0,max_obj+1):
+ for j in range(0,max_obj+1):
+ for k in range(0,max_obj+1):
+ q = q.append(pd.Series([ini_q]*len(actions),
+ index=q.columns,
+ name=(i,j,k)))
+
+ return q
+
+ def initE(self, ini_q, max_obj=7):
+ actions=[]
+ for i in range(1,4):
+ for j in range(1,max_obj+1):
+ actions.append((i,j))
+
+ self.actions = actions
+
+ q = pd.DataFrame(columns=actions, dtype=np.float64)
+
+ for i in range(0,max_obj+1):
+ for j in range(0,max_obj+1):
+ for k in range(0,max_obj+1):
+ q = q.append(pd.Series([ini_q]*len(actions),
+ index=q.columns,
+ name=(i,j,k)))
+
+ return q
+
+
+ def learnQ(self, delta):
+ self.q = self.q + self.alpha*delta*self.e
+ self.e = self.gamma*self.lambda_*self.e
+
+ def decide(self, state):
+ elig_actions = [a for a in self.actions if state[a[0]-1] >= a[1]]
+ if random.random() < self.epsilon:
+ action = random.choice(elig_actions)
+ else:
+ q = [self.getQ(state, a) for a in self.actions if state[a[0]-1] >= a[1]]
+ maxQ = max(q)
+ count = q.count(maxQ)
+ if count > 1:
+ best = [i for i in range(len(elig_actions)) if q[i] == maxQ]
+ i = random.choice(best)
+ else:
+ i = q.index(maxQ)
+ action = elig_actions[i]
+ return list(action)
+
+ def exploit(self, state):
+ elig_actions = [a for a in self.actions if state[a[0]-1] >= a[1]]
+ q = [self.getQ(state, a) for a in self.actions if state[a[0]-1] >= a[1]]
+ maxQ = max(q)
+ count = q.count(maxQ)
+ if count > 1:
+ best = [i for i in range(len(elig_actions)) if q[i] == maxQ]
+ i = random.choice(best)
+ else:
+ i = q.index(maxQ)
+ action = elig_actions[i]
+ return list(action)
+
+ def learn(self, state1, action1, reward, state2=[0,0,0], action2=None):
+ # print('learning')
+ if state2 == [0, 0, 0]:
+ qnext = 0.0 # terminal state
+ else:
+ qnext = self.getQ(state2, action2)
+ delta = reward + self.gamma * qnext - self.getQ(state1, action1)
+ newE = self.getE(state1, action1) + 1
+ self.setE(state1, action1, newE)
+ self.learnQ(delta)
diff --git a/train2.ipynb b/train2.ipynb
new file mode 100644
index 0000000..5659a05
--- /dev/null
+++ b/train2.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Sarsa with eligibility traces\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nim_env import NimEnv\n",
+ "from utils import compute_nim_sum, optimal_policy, random_policy\n",
+ "import random\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from sarsa import RLAgent\n",
+ "from sarsa_lambda import SARSA_LAMBDA\n",
+ "from montecarlo import MonteCarlo\n",
+ "from tqdm import tqdm\n",
+ "import pandas as pd\n",
+ "pd.options.display.float_format = '{:,.3f}'.format\n",
+ "\n",
+ "# plt figure setup\n",
+ "from matplotlib import rc\n",
+ "\n",
+ "plt.rc('axes', labelsize=14) # fontsize of the x and y labels\n",
+ "plt.rc('axes', titlesize=14)\n",
+ "plt.rc('xtick', labelsize=13) # fontsize of the tick labels\n",
+ "plt.rc('ytick', labelsize=13)\n",
+ "plt.rc('legend', fontsize=14) # legend fontsize\n",
+ "plt.rc('figure', titlesize=14) # fontsize of the figure title\n",
+ "plt.rc('lines', markersize=7)\n",
+ "plt.rc('lines', linewidth=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "\n",
+ "def evaluate_fixed(agent, N):\n",
+ " player0_ep_rewards = [0]*N\n",
+ " player1_ep_rewards = [0]*N\n",
+ " for i in range(N):\n",
+ " done = False\n",
+ " heaps = random.sample(range(1, 8), 3)\n",
+ " env = NimEnv(heaps)\n",
+ " turn = 0\n",
+ " while not done:\n",
+ " action = agent.decide(heaps)\n",
+ " next_heaps, winner, reward, done, turn = env.step(action)\n",
+ " if done:\n",
+ " break\n",
+ " adv_action = optimal_policy(next_heaps, randomness=0.2)\n",
+ " nextnext_heaps, winner, adv_reward, done, _ = env.step(adv_action)\n",
+ " heaps = nextnext_heaps\n",
+ "\n",
+ "\n",
+ " if winner == 0:\n",
+ " player0_ep_rewards.append(1) \n",
+ " player1_ep_rewards.append(-1) \n",
+ " else: # if winner == 1 \n",
+ " player0_ep_rewards.append(-1)\n",
+ " player1_ep_rewards.append(1) \n",
+ "\n",
+ " return np.mean(player0_ep_rewards), np.mean(player1_ep_rewards)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "heaps = [7,7,7]\n",
+ "env = NimEnv(heaps)\n",
+ "\n",
+ "\n",
+ "def run_experiment(env, num_episodes):\n",
+ " player0_rewards = []\n",
+ " player1_rewards = []\n",
+ " agent0 = SARSA_LAMBDA() # RL agent for player 0\n",
+ " # agent1 = RLAgent() # RL agent for player 1\n",
+ "\n",
+ " for episode in tqdm(range(1, num_episodes+1)):\n",
+ " heaps = env.reset() \n",
+ " env = NimEnv(heaps)\n",
+ " \n",
+ " winner = None\n",
+ " done = False\n",
+ "\n",
+ " action = None\n",
+ " reward = None\n",
+ " old_0 = (None, None) # old state/action for player0\n",
+ " old_reward = None\n",
+ " i = 0\n",
+ " agent0.resetE() # reset elig traces to 0\n",
+ " while not done:\n",
+ " action = agent0.decide(heaps)\n",
+ " next_heaps, winner, reward, done, _ = env.step(action)\n",
+ " if i > 0:\n",
+ " agent0.learn(old_0[0], old_0[1], old_reward, heaps, action)\n",
+ " if done:\n",
+ " agent0.learn(heaps, action, reward[0])\n",
+ " break\n",
+ " \n",
+ " adv_action = optimal_policy(next_heaps, randomness=0.2)\n",
+ " nextnext_heaps, winner, adv_reward, done, _ = env.step(adv_action)\n",
+ " if done:\n",
+ " agent0.learn(heaps, action, adv_reward[0])\n",
+ " break\n",
+ " \n",
+ " old_0 = heaps, action\n",
+ " old_reward = adv_reward[0]\n",
+ " heaps = nextnext_heaps\n",
+ " i += 1\n",
+ "\n",
+ " r0, r1 = evaluate_fixed(agent0, 10)\n",
+ " player0_rewards.append(r0) \n",
+ " player1_rewards.append(r1) \n",
+ " \n",
+ " return player0_rewards, player1_rewards, agent0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 50000/50000 [06:13<00:00, 133.86it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_episodes = 50000\n",
+ "r0, r1, agent = run_experiment(env, num_episodes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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