Source code for stable_baselines3.td3.policies

from typing import Any, Dict, List, Optional, Type, 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)


[docs]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)
[docs] 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)
[docs] 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
[docs]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, )
[docs]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, )