Page Menu
Home
c4science
Search
Configure Global Search
Log In
Files
F60631287
model.py
No One
Temporary
Actions
Download File
Edit File
Delete File
View Transforms
Subscribe
Mute Notifications
Award Token
Subscribers
None
File Metadata
Details
File Info
Storage
Attached
Created
Wed, May 1, 14:30
Size
2 KB
Mime Type
text/x-python
Expires
Fri, May 3, 14:30 (2 d)
Engine
blob
Format
Raw Data
Handle
17386965
Attached To
rDRLGYRO PS: DRL for gyroscope control
model.py
View Options
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
)
Event Timeline
Log In to Comment