from typing import Any, Optional, Union import torch as th from gymnasium import spaces from torch import nn from stable_baselines3.common.policies import BasePolicy, ContinuousCritic from stable_baselines3.common.preprocessing import get_action_dim from stable_baselines3.common.torch_layers import ( BaseFeaturesExtractor, CombinedExtractor, FlattenExtractor, NatureCNN, create_mlp, get_actor_critic_arch, ) from stable_baselines3.common.type_aliases import PyTorchObs, Schedule class Actor(BasePolicy): """ Actor network (policy) for TD3. :param observation_space: Observation space :param action_space: Action space :param net_arch: Network architecture :param features_extractor: Network to extract features (a CNN when using images, a nn.Flatten() layer otherwise) :param features_dim: Number of features :param activation_fn: Activation function :param normalize_images: Whether to normalize images or not, dividing by 255.0 (True by default) """ def __init__( self, observation_space: spaces.Space, action_space: spaces.Box, net_arch: list[int], features_extractor: nn.Module, features_dim: int, activation_fn: type[nn.Module] = nn.ReLU, normalize_images: bool = True, ): super().__init__( observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images, squash_output=True, ) self.net_arch = net_arch self.features_dim = features_dim self.activation_fn = activation_fn action_dim = get_action_dim(self.action_space) actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True) # Deterministic action self.mu = nn.Sequential(*actor_net) def _get_constructor_parameters(self) -> dict[str, Any]: data = super()._get_constructor_parameters() data.update( dict( net_arch=self.net_arch, features_dim=self.features_dim, activation_fn=self.activation_fn, features_extractor=self.features_extractor, ) ) return data def forward(self, obs: th.Tensor) -> th.Tensor: # assert deterministic, 'The TD3 actor only outputs deterministic actions' features = self.extract_features(obs, self.features_extractor) return self.mu(features) def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor: # Note: the deterministic deterministic parameter is ignored in the case of TD3. # Predictions are always deterministic. return self(observation) class TD3Policy(BasePolicy): """ Policy class (with both actor and critic) for TD3. :param observation_space: Observation space :param action_space: Action space :param lr_schedule: Learning rate schedule (could be constant) :param net_arch: The specification of the policy and value networks. :param activation_fn: Activation function :param features_extractor_class: Features extractor to use. :param features_extractor_kwargs: Keyword arguments to pass to the features extractor. :param normalize_images: Whether to normalize images or not, dividing by 255.0 (True by default) :param optimizer_class: The optimizer to use, ``th.optim.Adam`` by default :param optimizer_kwargs: Additional keyword arguments, excluding the learning rate, to pass to the optimizer :param n_critics: Number of critic networks to create. :param share_features_extractor: Whether to share or not the features extractor between the actor and the critic (this saves computation time) """ actor: Actor actor_target: Actor critic: ContinuousCritic critic_target: ContinuousCritic def __init__( self, observation_space: spaces.Space, action_space: spaces.Box, lr_schedule: Schedule, net_arch: Optional[Union[list[int], dict[str, list[int]]]] = None, 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, n_critics: int = 2, share_features_extractor: bool = False, ): super().__init__( observation_space, action_space, features_extractor_class, features_extractor_kwargs, optimizer_class=optimizer_class, optimizer_kwargs=optimizer_kwargs, squash_output=True, normalize_images=normalize_images, ) # Default network architecture, from the original paper if net_arch is None: if features_extractor_class == NatureCNN: net_arch = [256, 256] else: net_arch = [400, 300] actor_arch, critic_arch = get_actor_critic_arch(net_arch) 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": actor_arch, "activation_fn": self.activation_fn, "normalize_images": normalize_images, } self.actor_kwargs = self.net_args.copy() self.critic_kwargs = self.net_args.copy() self.critic_kwargs.update( { "n_critics": n_critics, "net_arch": critic_arch, "share_features_extractor": share_features_extractor, } ) self.share_features_extractor = share_features_extractor self._build(lr_schedule) def _build(self, lr_schedule: Schedule) -> None: # Create actor and target # the features extractor should not be shared self.actor = self.make_actor(features_extractor=None) self.actor_target = self.make_actor(features_extractor=None) # Initialize the target to have the same weights as the actor self.actor_target.load_state_dict(self.actor.state_dict()) self.actor.optimizer = self.optimizer_class( self.actor.parameters(), lr=lr_schedule(1), # type: ignore[call-arg] **self.optimizer_kwargs, ) if self.share_features_extractor: self.critic = self.make_critic(features_extractor=self.actor.features_extractor) # Critic target should not share the features extractor with critic # but it can share it with the actor target as actor and critic are sharing # the same features_extractor too # NOTE: as a result the effective poliak (soft-copy) coefficient for the features extractor # will be 2 * tau instead of tau (updated one time with the actor, a second time with the critic) self.critic_target = self.make_critic(features_extractor=self.actor_target.features_extractor) else: # Create new features extractor for each network self.critic = self.make_critic(features_extractor=None) self.critic_target = self.make_critic(features_extractor=None) self.critic_target.load_state_dict(self.critic.state_dict()) self.critic.optimizer = self.optimizer_class( self.critic.parameters(), lr=lr_schedule(1), # type: ignore[call-arg] **self.optimizer_kwargs, ) # Target networks should always be in eval mode self.actor_target.set_training_mode(False) self.critic_target.set_training_mode(False) def _get_constructor_parameters(self) -> dict[str, Any]: data = super()._get_constructor_parameters() data.update( dict( net_arch=self.net_arch, activation_fn=self.net_args["activation_fn"], n_critics=self.critic_kwargs["n_critics"], 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, share_features_extractor=self.share_features_extractor, ) ) return data def make_actor(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> Actor: actor_kwargs = self._update_features_extractor(self.actor_kwargs, features_extractor) return Actor(**actor_kwargs).to(self.device) def make_critic(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> ContinuousCritic: critic_kwargs = self._update_features_extractor(self.critic_kwargs, features_extractor) return ContinuousCritic(**critic_kwargs).to(self.device) def forward(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor: return self._predict(observation, deterministic=deterministic) def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor: # Note: the deterministic deterministic parameter is ignored in the case of TD3. # Predictions are always deterministic. return self.actor(observation) 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. :param mode: if true, set to training mode, else set to evaluation mode """ self.actor.set_training_mode(mode) self.critic.set_training_mode(mode) self.training = mode MlpPolicy = TD3Policy class CnnPolicy(TD3Policy): """ Policy class (with both actor and critic) for TD3. :param observation_space: Observation space :param action_space: Action space :param lr_schedule: Learning rate schedule (could be constant) :param net_arch: The specification of the policy and value networks. :param activation_fn: Activation function :param features_extractor_class: Features extractor to use. :param features_extractor_kwargs: Keyword arguments to pass to the features extractor. :param normalize_images: Whether to normalize images or not, dividing by 255.0 (True by default) :param optimizer_class: The optimizer to use, ``th.optim.Adam`` by default :param optimizer_kwargs: Additional keyword arguments, excluding the learning rate, to pass to the optimizer :param n_critics: Number of critic networks to create. :param share_features_extractor: Whether to share or not the features extractor between the actor and the critic (this saves computation time) """ def __init__( self, observation_space: spaces.Space, action_space: spaces.Box, lr_schedule: Schedule, net_arch: Optional[Union[list[int], dict[str, list[int]]]] = None, 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, n_critics: int = 2, share_features_extractor: bool = False, ): super().__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, n_critics, share_features_extractor, ) class MultiInputPolicy(TD3Policy): """ Policy class (with both actor and critic) for TD3 to be used with Dict observation spaces. :param observation_space: Observation space :param action_space: Action space :param lr_schedule: Learning rate schedule (could be constant) :param net_arch: The specification of the policy and value networks. :param activation_fn: Activation function :param features_extractor_class: Features extractor to use. :param features_extractor_kwargs: Keyword arguments to pass to the features extractor. :param normalize_images: Whether to normalize images or not, dividing by 255.0 (True by default) :param optimizer_class: The optimizer to use, ``th.optim.Adam`` by default :param optimizer_kwargs: Additional keyword arguments, excluding the learning rate, to pass to the optimizer :param n_critics: Number of critic networks to create. :param share_features_extractor: Whether to share or not the features extractor between the actor and the critic (this saves computation time) """ def __init__( self, observation_space: spaces.Dict, action_space: spaces.Box, lr_schedule: Schedule, net_arch: Optional[Union[list[int], dict[str, list[int]]]] = None, activation_fn: type[nn.Module] = nn.ReLU, features_extractor_class: type[BaseFeaturesExtractor] = CombinedExtractor, 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, n_critics: int = 2, share_features_extractor: bool = False, ): super().__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, n_critics, share_features_extractor, )