from typing import Any, Callable, Dict, List, Optional, Type, Union
import gym
import torch as th
from torch import nn as nn
from stable_baselines3.common.policies import BasePolicy, register_policy
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
class QNetwork(BasePolicy):
"""
Action-Value (Q-Value) network for DQN
:param observation_space: (gym.spaces.Space) Observation space
:param action_space: (gym.spaces.Space) Action space
:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
:param device: (str or th.device) Device on which the code should run.
:param activation_fn: (Type[nn.Module]) Activation function
:param normalize_images: (bool) Whether to normalize images or not,
dividing by 255.0 (True by default)
"""
def __init__(
self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
features_extractor: nn.Module,
features_dim: int,
net_arch: Optional[List[int]] = None,
device: Union[th.device, str] = "auto",
activation_fn: Type[nn.Module] = nn.ReLU,
normalize_images: bool = True,
):
super(QNetwork, self).__init__(
observation_space,
action_space,
features_extractor=features_extractor,
normalize_images=normalize_images,
device=device,
)
if net_arch is None:
net_arch = [64, 64]
self.net_arch = net_arch
self.activation_fn = activation_fn
self.features_extractor = features_extractor
self.features_dim = features_dim
self.normalize_images = normalize_images
action_dim = 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: th.Tensor) -> th.Tensor:
"""
Predict the q-values.
:param obs: (th.Tensor) Observation
:return: (th.Tensor) The estimated Q-Value for each action.
"""
return self.q_net(self.extract_features(obs))
def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
q_values = self.forward(observation)
# Greedy action
action = q_values.argmax(dim=1).reshape(-1)
return action
def _get_data(self) -> Dict[str, Any]:
data = super()._get_data()
data.update(
dict(
net_arch=self.net_arch,
features_dim=self.features_dim,
activation_fn=self.activation_fn,
features_extractor=self.features_extractor,
epsilon=self.epsilon,
)
)
return data
class DQNPolicy(BasePolicy):
"""
Policy class with Q-Value Net and target net for DQN
:param observation_space: (gym.spaces.Space) Observation space
:param action_space: (gym.spaces.Space) Action space
:param lr_schedule: (callable) Learning rate schedule (could be constant)
:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
:param device: (str or th.device) Device on which the code should run.
:param activation_fn: (Type[nn.Module]) Activation function
:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
to pass to the feature extractor.
:param normalize_images: (bool) Whether to normalize images or not,
dividing by 255.0 (True by default)
:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
``th.optim.Adam`` by default
:param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
excluding the learning rate, to pass to the optimizer
"""
def __init__(
self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
lr_schedule: Callable,
net_arch: Optional[List[int]] = None,
device: Union[th.device, str] = "auto",
activation_fn: Type[nn.Module] = nn.ReLU,
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
normalize_images: bool = True,
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
optimizer_kwargs: Optional[Dict[str, Any]] = None,
):
super(DQNPolicy, self).__init__(
observation_space,
action_space,
device,
features_extractor_class,
features_extractor_kwargs,
optimizer_class=optimizer_class,
optimizer_kwargs=optimizer_kwargs,
)
if net_arch is None:
if features_extractor_class == FlattenExtractor:
net_arch = [64, 64]
else:
net_arch = []
self.net_arch = net_arch
self.activation_fn = activation_fn
self.normalize_images = normalize_images
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,
"device": device,
}
self.q_net, self.q_net_target = None, None
self._build(lr_schedule)
def _build(self, lr_schedule: Callable) -> None:
"""
Create the network and the optimizer.
:param lr_schedule: (Callable) 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())
# Setup optimizer with initial learning rate
self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
def make_q_net(self) -> QNetwork:
# Make sure we always have separate networks for feature extractors etc
features_extractor = self.features_extractor_class(self.observation_space, **self.features_extractor_kwargs)
features_dim = features_extractor.features_dim
return QNetwork(features_extractor=features_extractor, features_dim=features_dim, **self.net_args).to(self.device)
def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
return self._predict(obs, deterministic=deterministic)
def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
return self.q_net._predict(obs, deterministic=deterministic)
def _get_data(self) -> Dict[str, Any]:
data = super()._get_data()
data.update(
dict(
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
MlpPolicy = DQNPolicy
[docs]class CnnPolicy(DQNPolicy):
"""
Policy class for DQN when using images as input.
:param observation_space: (gym.spaces.Space) Observation space
:param action_space: (gym.spaces.Space) Action space
:param lr_schedule: (callable) Learning rate schedule (could be constant)
:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
:param device: (str or th.device) Device on which the code should run.
:param activation_fn: (Type[nn.Module]) Activation function
:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
:param normalize_images: (bool) Whether to normalize images or not,
dividing by 255.0 (True by default)
:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
``th.optim.Adam`` by default
:param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
excluding the learning rate, to pass to the optimizer
"""
def __init__(
self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
lr_schedule: Callable,
net_arch: Optional[List[int]] = None,
device: Union[th.device, str] = "auto",
activation_fn: Type[nn.Module] = nn.ReLU,
features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
normalize_images: bool = True,
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
optimizer_kwargs: Optional[Dict[str, Any]] = None,
):
super(CnnPolicy, self).__init__(
observation_space,
action_space,
lr_schedule,
net_arch,
device,
activation_fn,
features_extractor_class,
features_extractor_kwargs,
normalize_images,
optimizer_class,
optimizer_kwargs,
)
register_policy("MlpPolicy", MlpPolicy)
register_policy("CnnPolicy", CnnPolicy)