1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302 | from typing import Any, Optional
from gymnasium import spaces
import torch
from torch import nn
from lit_sb3.core.types import PyTorchObs, Schedule
from lit_sb3.modules import (
BaseFeaturesExtractor,
CombinedExtractor,
FlattenExtractor,
NatureCNN,
create_mlp,
)
from lit_sb3.policies import BasePolicy
class QNetwork(BasePolicy):
"""Action-Value (Q-Value) network for DQN.
Args:
observation_space: Observation space
action_space: Action space
net_arch: The specification of the policy and value networks.
activation_fn: Activation function
normalize_images: Whether to normalize images or not,
dividing by 255.0 (True by default)
"""
action_space: spaces.Discrete
def __init__(
self,
observation_space: spaces.Space,
action_space: spaces.Discrete,
features_extractor: BaseFeaturesExtractor,
features_dim: int,
net_arch: list[int] | None = None,
activation_fn: type[nn.Module] = nn.ReLU,
normalize_images: bool = True,
) -> None:
super().__init__(
observation_space,
action_space,
features_extractor=features_extractor,
normalize_images=normalize_images,
)
if net_arch is None:
net_arch = [64, 64]
self.net_arch = net_arch
self.activation_fn = activation_fn
self.features_dim = features_dim
action_dim = int(self.action_space.n) # number of actions
q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
self.q_net = nn.Sequential(*q_net)
def forward(self, obs: PyTorchObs) -> torch.Tensor:
"""Predict the q-values.
Args:
obs: Observation
Returns:
The estimated Q-Value for each action.
"""
return self.q_net(self.extract_features(obs, self.features_extractor))
def _predict(self, observation: PyTorchObs, deterministic: bool = True) -> torch.Tensor:
q_values = self(observation)
# Greedy action
action = q_values.argmax(dim=1).reshape(-1)
return action
def _get_constructor_parameters(self) -> dict[str, Any]:
data = super()._get_constructor_parameters()
data.update(
{
"net_arch": self.net_arch,
"features_dim": self.features_dim,
"activation_fn": self.activation_fn,
"features_extractor": self.features_extractor,
}
)
return data
class DQNPolicy(BasePolicy):
"""Policy class with Q-Value Net and target net for DQN.
Args:
observation_space: Observation space
action_space: Action space
lr_schedule: Learning rate schedule (could be constant)
net_arch: The specification of the policy and value networks.
activation_fn: Activation function
features_extractor_class: Features extractor to use.
features_extractor_kwargs: Keyword arguments
to pass to the features extractor.
normalize_images: Whether to normalize images or not,
dividing by 255.0 (True by default)
optimizer_class: The optimizer to use,
``torch.optim.Adam`` by default
optimizer_kwargs: Additional keyword arguments,
excluding the learning rate, to pass to the optimizer
"""
q_net: QNetwork
q_net_target: QNetwork
def __init__(
self,
observation_space: spaces.Space,
action_space: spaces.Discrete,
lr_schedule: Schedule,
net_arch: list[int] | None = None,
activation_fn: type[nn.Module] = nn.ReLU,
features_extractor_class: type[BaseFeaturesExtractor] = FlattenExtractor,
features_extractor_kwargs: dict[str, Any] | None = None,
normalize_images: bool = True,
optimizer_class: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_kwargs: dict[str, Any] | None = None,
) -> None:
super().__init__(
observation_space,
action_space,
features_extractor_class,
features_extractor_kwargs,
optimizer_class=optimizer_class,
optimizer_kwargs=optimizer_kwargs,
normalize_images=normalize_images,
)
if net_arch is None:
net_arch = [] if features_extractor_class == NatureCNN else [64, 64]
self.net_arch = net_arch
self.activation_fn = activation_fn
self.net_args = {
"observation_space": self.observation_space,
"action_space": self.action_space,
"net_arch": self.net_arch,
"activation_fn": self.activation_fn,
"normalize_images": normalize_images,
}
self._build(lr_schedule)
def _build(self, lr_schedule: Schedule) -> None:
"""Create the network and the optimizer.
Put the target network into evaluation mode.
Args:
lr_schedule: Learning rate schedule
lr_schedule(1) is the initial learning rate
"""
self.q_net = self.make_q_net()
self.q_net_target = self.make_q_net()
self.q_net_target.load_state_dict(self.q_net.state_dict())
self.q_net_target.set_training_mode(False)
# Setup optimizer with initial learning rate
self.optimizer = self.optimizer_class(
self.q_net.parameters(),
lr=lr_schedule(1), # ty:ignore[unknown-argument]
**self.optimizer_kwargs,
)
def make_q_net(self) -> QNetwork:
# Make sure we always have separate networks for features extractors etc
net_args = self._update_features_extractor(self.net_args, features_extractor=None)
return QNetwork(**net_args).to(self.device)
def forward(self, obs: PyTorchObs, deterministic: bool = True) -> torch.Tensor:
return self._predict(obs, deterministic=deterministic)
def _predict(self, obs: PyTorchObs, deterministic: bool = True) -> torch.Tensor: # ty:ignore[invalid-method-override]
return self.q_net._predict(obs, deterministic=deterministic)
def _get_constructor_parameters(self) -> dict[str, Any]:
data = super()._get_constructor_parameters()
data.update(
{
"net_arch": self.net_args["net_arch"],
"activation_fn": self.net_args["activation_fn"],
"lr_schedule": self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
"optimizer_class": self.optimizer_class,
"optimizer_kwargs": self.optimizer_kwargs,
"features_extractor_class": self.features_extractor_class,
"features_extractor_kwargs": self.features_extractor_kwargs,
}
)
return data
def set_training_mode(self, mode: bool) -> None:
"""Put the policy in either training or evaluation mode.
This affects certain modules, such as batch normalisation and dropout.
Args:
mode: if true, set to training mode, else set to evaluation mode
"""
self.q_net.set_training_mode(mode)
self.training = mode
MlpPolicy = DQNPolicy
class CnnPolicy(DQNPolicy):
"""Policy class for DQN when using images as input.
Args:
observation_space: Observation space
action_space: Action space
lr_schedule: Learning rate schedule (could be constant)
net_arch: The specification of the policy and value networks.
activation_fn: Activation function
features_extractor_class: Features extractor to use.
normalize_images: Whether to normalize images or not,
dividing by 255.0 (True by default)
optimizer_class: The optimizer to use,
``torch.optim.Adam`` by default
optimizer_kwargs: Additional keyword arguments,
excluding the learning rate, to pass to the optimizer
"""
def __init__(
self,
observation_space: spaces.Space,
action_space: spaces.Discrete,
lr_schedule: Schedule,
net_arch: list[int] | None = None,
activation_fn: type[nn.Module] = nn.ReLU,
features_extractor_class: type[BaseFeaturesExtractor] = NatureCNN,
features_extractor_kwargs: dict[str, Any] | None = None,
normalize_images: bool = True,
optimizer_class: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_kwargs: dict[str, Any] | None = None,
) -> None:
super().__init__(
observation_space,
action_space,
lr_schedule,
net_arch,
activation_fn,
features_extractor_class,
features_extractor_kwargs,
normalize_images,
optimizer_class,
optimizer_kwargs,
)
class MultiInputPolicy(DQNPolicy):
"""Policy class for DQN when using dict observations as input.
Args:
observation_space: Observation space
action_space: Action space
lr_schedule: Learning rate schedule (could be constant)
net_arch: The specification of the policy and value networks.
activation_fn: Activation function
features_extractor_class: Features extractor to use.
normalize_images: Whether to normalize images or not,
dividing by 255.0 (True by default)
optimizer_class: The optimizer to use,
``torch.optim.Adam`` by default
optimizer_kwargs: Additional keyword arguments,
excluding the learning rate, to pass to the optimizer
"""
def __init__(
self,
observation_space: spaces.Dict,
action_space: spaces.Discrete,
lr_schedule: Schedule,
net_arch: list[int] | None = None,
activation_fn: type[nn.Module] = nn.ReLU,
features_extractor_class: type[BaseFeaturesExtractor] = CombinedExtractor,
features_extractor_kwargs: dict[str, Any] | None = None,
normalize_images: bool = True,
optimizer_class: type[torch.optim.Optimizer] = torch.optim.Adam,
optimizer_kwargs: dict[str, Any] | None = None,
) -> None:
super().__init__(
observation_space,
action_space,
lr_schedule,
net_arch,
activation_fn,
features_extractor_class,
features_extractor_kwargs,
normalize_images,
optimizer_class,
optimizer_kwargs,
)
|