Source code for stable_baselines3.common.policies

"""Policies: abstract base class and concrete implementations."""

import collections
import copy
from abc import ABC, abstractmethod
from functools import partial
from typing import Any, Dict, List, Optional, Tuple, Type, TypeVar, Union

import gym
import numpy as np
import torch as th
from torch import nn

from stable_baselines3.common.distributions import (
    BernoulliDistribution,
    CategoricalDistribution,
    DiagGaussianDistribution,
    Distribution,
    MultiCategoricalDistribution,
    StateDependentNoiseDistribution,
    make_proba_distribution,
)
from stable_baselines3.common.preprocessing import get_action_dim, is_image_space, maybe_transpose, preprocess_obs
from stable_baselines3.common.torch_layers import (
    BaseFeaturesExtractor,
    CombinedExtractor,
    FlattenExtractor,
    MlpExtractor,
    NatureCNN,
    create_mlp,
)
from stable_baselines3.common.type_aliases import Schedule
from stable_baselines3.common.utils import get_device, is_vectorized_observation, obs_as_tensor

SelfBaseModel = TypeVar("SelfBaseModel", bound="BaseModel")


class BaseModel(nn.Module):
    """
    The base model object: makes predictions in response to observations.

    In the case of policies, the prediction is an action. In the case of critics, it is the
    estimated value of the observation.

    :param observation_space: The observation space of the environment
    :param action_space: The action space of the environment
    :param features_extractor_class: Features extractor to use.
    :param features_extractor_kwargs: Keyword arguments
        to pass to the features extractor.
    :param features_extractor: Network to extract features
        (a CNN when using images, a nn.Flatten() layer otherwise)
    :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
    """

    def __init__(
        self,
        observation_space: gym.spaces.Space,
        action_space: gym.spaces.Space,
        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
        features_extractor: Optional[nn.Module] = None,
        normalize_images: bool = True,
        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
        optimizer_kwargs: Optional[Dict[str, Any]] = None,
    ):
        super().__init__()

        if optimizer_kwargs is None:
            optimizer_kwargs = {}

        if features_extractor_kwargs is None:
            features_extractor_kwargs = {}

        self.observation_space = observation_space
        self.action_space = action_space
        self.features_extractor = features_extractor
        self.normalize_images = normalize_images

        self.optimizer_class = optimizer_class
        self.optimizer_kwargs = optimizer_kwargs
        self.optimizer = None  # type: Optional[th.optim.Optimizer]

        self.features_extractor_class = features_extractor_class
        self.features_extractor_kwargs = features_extractor_kwargs

    def _update_features_extractor(
        self,
        net_kwargs: Dict[str, Any],
        features_extractor: Optional[BaseFeaturesExtractor] = None,
    ) -> Dict[str, Any]:
        """
        Update the network keyword arguments and create a new features extractor object if needed.
        If a ``features_extractor`` object is passed, then it will be shared.

        :param net_kwargs: the base network keyword arguments, without the ones
            related to features extractor
        :param features_extractor: a features extractor object.
            If None, a new object will be created.
        :return: The updated keyword arguments
        """
        net_kwargs = net_kwargs.copy()
        if features_extractor is None:
            # The features extractor is not shared, create a new one
            features_extractor = self.make_features_extractor()
        net_kwargs.update(dict(features_extractor=features_extractor, features_dim=features_extractor.features_dim))
        return net_kwargs

    def make_features_extractor(self) -> BaseFeaturesExtractor:
        """Helper method to create a features extractor."""
        return self.features_extractor_class(self.observation_space, **self.features_extractor_kwargs)

    def extract_features(self, obs: th.Tensor) -> th.Tensor:
        """
        Preprocess the observation if needed and extract features.

        :param obs:
        :return:
        """
        assert self.features_extractor is not None, "No features extractor was set"
        preprocessed_obs = preprocess_obs(obs, self.observation_space, normalize_images=self.normalize_images)
        return self.features_extractor(preprocessed_obs)

    def _get_constructor_parameters(self) -> Dict[str, Any]:
        """
        Get data that need to be saved in order to re-create the model when loading it from disk.

        :return: The dictionary to pass to the as kwargs constructor when reconstruction this model.
        """
        return dict(
            observation_space=self.observation_space,
            action_space=self.action_space,
            # Passed to the constructor by child class
            # squash_output=self.squash_output,
            # features_extractor=self.features_extractor
            normalize_images=self.normalize_images,
        )

    @property
    def device(self) -> th.device:
        """Infer which device this policy lives on by inspecting its parameters.
        If it has no parameters, the 'cpu' device is used as a fallback.

        :return:"""
        for param in self.parameters():
            return param.device
        return get_device("cpu")

    def save(self, path: str) -> None:
        """
        Save model to a given location.

        :param path:
        """
        th.save({"state_dict": self.state_dict(), "data": self._get_constructor_parameters()}, path)

    @classmethod
    def load(cls: Type[SelfBaseModel], path: str, device: Union[th.device, str] = "auto") -> SelfBaseModel:
        """
        Load model from path.

        :param path:
        :param device: Device on which the policy should be loaded.
        :return:
        """
        device = get_device(device)
        saved_variables = th.load(path, map_location=device)

        # Create policy object
        model = cls(**saved_variables["data"])  # pytype: disable=not-instantiable
        # Load weights
        model.load_state_dict(saved_variables["state_dict"])
        model.to(device)
        return model

    def load_from_vector(self, vector: np.ndarray) -> None:
        """
        Load parameters from a 1D vector.

        :param vector:
        """
        th.nn.utils.vector_to_parameters(th.FloatTensor(vector).to(self.device), self.parameters())

    def parameters_to_vector(self) -> np.ndarray:
        """
        Convert the parameters to a 1D vector.

        :return:
        """
        return th.nn.utils.parameters_to_vector(self.parameters()).detach().cpu().numpy()

    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.train(mode)

    def obs_to_tensor(self, observation: Union[np.ndarray, Dict[str, np.ndarray]]) -> Tuple[th.Tensor, bool]:
        """
        Convert an input observation to a PyTorch tensor that can be fed to a model.
        Includes sugar-coating to handle different observations (e.g. normalizing images).

        :param observation: the input observation
        :return: The observation as PyTorch tensor
            and whether the observation is vectorized or not
        """
        vectorized_env = False
        if isinstance(observation, dict):
            # need to copy the dict as the dict in VecFrameStack will become a torch tensor
            observation = copy.deepcopy(observation)
            for key, obs in observation.items():
                obs_space = self.observation_space.spaces[key]
                if is_image_space(obs_space):
                    obs_ = maybe_transpose(obs, obs_space)
                else:
                    obs_ = np.array(obs)
                vectorized_env = vectorized_env or is_vectorized_observation(obs_, obs_space)
                # Add batch dimension if needed
                observation[key] = obs_.reshape((-1,) + self.observation_space[key].shape)

        elif is_image_space(self.observation_space):
            # Handle the different cases for images
            # as PyTorch use channel first format
            observation = maybe_transpose(observation, self.observation_space)

        else:
            observation = np.array(observation)

        if not isinstance(observation, dict):
            # Dict obs need to be handled separately
            vectorized_env = is_vectorized_observation(observation, self.observation_space)
            # Add batch dimension if needed
            observation = observation.reshape((-1,) + self.observation_space.shape)

        observation = obs_as_tensor(observation, self.device)
        return observation, vectorized_env


class BasePolicy(BaseModel, ABC):
    """The base policy object.

    Parameters are mostly the same as `BaseModel`; additions are documented below.

    :param args: positional arguments passed through to `BaseModel`.
    :param kwargs: keyword arguments passed through to `BaseModel`.
    :param squash_output: For continuous actions, whether the output is squashed
        or not using a ``tanh()`` function.
    """

    def __init__(self, *args, squash_output: bool = False, **kwargs):
        super().__init__(*args, **kwargs)
        self._squash_output = squash_output

    @staticmethod
    def _dummy_schedule(progress_remaining: float) -> float:
        """(float) Useful for pickling policy."""
        del progress_remaining
        return 0.0

    @property
    def squash_output(self) -> bool:
        """(bool) Getter for squash_output."""
        return self._squash_output

    @staticmethod
    def init_weights(module: nn.Module, gain: float = 1) -> None:
        """
        Orthogonal initialization (used in PPO and A2C)
        """
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            nn.init.orthogonal_(module.weight, gain=gain)
            if module.bias is not None:
                module.bias.data.fill_(0.0)

    @abstractmethod
    def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
        """
        Get the action according to the policy for a given observation.

        By default provides a dummy implementation -- not all BasePolicy classes
        implement this, e.g. if they are a Critic in an Actor-Critic method.

        :param observation:
        :param deterministic: Whether to use stochastic or deterministic actions
        :return: Taken action according to the policy
        """

    def predict(
        self,
        observation: Union[np.ndarray, Dict[str, np.ndarray]],
        state: Optional[Tuple[np.ndarray, ...]] = None,
        episode_start: Optional[np.ndarray] = None,
        deterministic: bool = False,
    ) -> Tuple[np.ndarray, Optional[Tuple[np.ndarray, ...]]]:
        """
        Get the policy action from an observation (and optional hidden state).
        Includes sugar-coating to handle different observations (e.g. normalizing images).

        :param observation: the input observation
        :param state: The last hidden states (can be None, used in recurrent policies)
        :param episode_start: The last masks (can be None, used in recurrent policies)
            this correspond to beginning of episodes,
            where the hidden states of the RNN must be reset.
        :param deterministic: Whether or not to return deterministic actions.
        :return: the model's action and the next hidden state
            (used in recurrent policies)
        """
        # TODO (GH/1): add support for RNN policies
        # if state is None:
        #     state = self.initial_state
        # if episode_start is None:
        #     episode_start = [False for _ in range(self.n_envs)]
        # Switch to eval mode (this affects batch norm / dropout)
        self.set_training_mode(False)

        observation, vectorized_env = self.obs_to_tensor(observation)

        with th.no_grad():
            actions = self._predict(observation, deterministic=deterministic)
        # Convert to numpy, and reshape to the original action shape
        actions = actions.cpu().numpy().reshape((-1,) + self.action_space.shape)

        if isinstance(self.action_space, gym.spaces.Box):
            if self.squash_output:
                # Rescale to proper domain when using squashing
                actions = self.unscale_action(actions)
            else:
                # Actions could be on arbitrary scale, so clip the actions to avoid
                # out of bound error (e.g. if sampling from a Gaussian distribution)
                actions = np.clip(actions, self.action_space.low, self.action_space.high)

        # Remove batch dimension if needed
        if not vectorized_env:
            actions = actions.squeeze(axis=0)

        return actions, state

    def scale_action(self, action: np.ndarray) -> np.ndarray:
        """
        Rescale the action from [low, high] to [-1, 1]
        (no need for symmetric action space)

        :param action: Action to scale
        :return: Scaled action
        """
        low, high = self.action_space.low, self.action_space.high
        return 2.0 * ((action - low) / (high - low)) - 1.0

    def unscale_action(self, scaled_action: np.ndarray) -> np.ndarray:
        """
        Rescale the action from [-1, 1] to [low, high]
        (no need for symmetric action space)

        :param scaled_action: Action to un-scale
        """
        low, high = self.action_space.low, self.action_space.high
        return low + (0.5 * (scaled_action + 1.0) * (high - low))


[docs]class ActorCriticPolicy(BasePolicy): """ Policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes. :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 ortho_init: Whether to use or not orthogonal initialization :param use_sde: Whether to use State Dependent Exploration or not :param log_std_init: Initial value for the log standard deviation :param full_std: Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDE :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough. :param squash_output: Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE. :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 """ def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None, activation_fn: Type[nn.Module] = nn.Tanh, ortho_init: bool = True, use_sde: bool = False, log_std_init: float = 0.0, full_std: bool = True, use_expln: bool = False, squash_output: bool = False, 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, ): if optimizer_kwargs is None: optimizer_kwargs = {} # Small values to avoid NaN in Adam optimizer if optimizer_class == th.optim.Adam: optimizer_kwargs["eps"] = 1e-5 super().__init__( observation_space, action_space, features_extractor_class, features_extractor_kwargs, optimizer_class=optimizer_class, optimizer_kwargs=optimizer_kwargs, squash_output=squash_output, ) # Default network architecture, from stable-baselines if net_arch is None: if features_extractor_class == NatureCNN: net_arch = [] else: net_arch = [dict(pi=[64, 64], vf=[64, 64])] self.net_arch = net_arch self.activation_fn = activation_fn self.ortho_init = ortho_init self.features_extractor = features_extractor_class(self.observation_space, **self.features_extractor_kwargs) self.features_dim = self.features_extractor.features_dim self.normalize_images = normalize_images self.log_std_init = log_std_init dist_kwargs = None # Keyword arguments for gSDE distribution if use_sde: dist_kwargs = { "full_std": full_std, "squash_output": squash_output, "use_expln": use_expln, "learn_features": False, } self.use_sde = use_sde self.dist_kwargs = dist_kwargs # Action distribution self.action_dist = make_proba_distribution(action_space, use_sde=use_sde, dist_kwargs=dist_kwargs) self._build(lr_schedule) def _get_constructor_parameters(self) -> Dict[str, Any]: data = super()._get_constructor_parameters() default_none_kwargs = self.dist_kwargs or collections.defaultdict(lambda: None) data.update( dict( net_arch=self.net_arch, activation_fn=self.activation_fn, use_sde=self.use_sde, log_std_init=self.log_std_init, squash_output=default_none_kwargs["squash_output"], full_std=default_none_kwargs["full_std"], use_expln=default_none_kwargs["use_expln"], lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone ortho_init=self.ortho_init, 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
[docs] def reset_noise(self, n_envs: int = 1) -> None: """ Sample new weights for the exploration matrix. :param n_envs: """ assert isinstance(self.action_dist, StateDependentNoiseDistribution), "reset_noise() is only available when using gSDE" self.action_dist.sample_weights(self.log_std, batch_size=n_envs)
def _build_mlp_extractor(self) -> None: """ Create the policy and value networks. Part of the layers can be shared. """ # Note: If net_arch is None and some features extractor is used, # net_arch here is an empty list and mlp_extractor does not # really contain any layers (acts like an identity module). self.mlp_extractor = MlpExtractor( self.features_dim, net_arch=self.net_arch, activation_fn=self.activation_fn, device=self.device, ) def _build(self, lr_schedule: Schedule) -> None: """ Create the networks and the optimizer. :param lr_schedule: Learning rate schedule lr_schedule(1) is the initial learning rate """ self._build_mlp_extractor() latent_dim_pi = self.mlp_extractor.latent_dim_pi if isinstance(self.action_dist, DiagGaussianDistribution): self.action_net, self.log_std = self.action_dist.proba_distribution_net( latent_dim=latent_dim_pi, log_std_init=self.log_std_init ) elif isinstance(self.action_dist, StateDependentNoiseDistribution): self.action_net, self.log_std = self.action_dist.proba_distribution_net( latent_dim=latent_dim_pi, latent_sde_dim=latent_dim_pi, log_std_init=self.log_std_init ) elif isinstance(self.action_dist, (CategoricalDistribution, MultiCategoricalDistribution, BernoulliDistribution)): self.action_net = self.action_dist.proba_distribution_net(latent_dim=latent_dim_pi) else: raise NotImplementedError(f"Unsupported distribution '{self.action_dist}'.") self.value_net = nn.Linear(self.mlp_extractor.latent_dim_vf, 1) # Init weights: use orthogonal initialization # with small initial weight for the output if self.ortho_init: # TODO: check for features_extractor # Values from stable-baselines. # features_extractor/mlp values are # originally from openai/baselines (default gains/init_scales). module_gains = { self.features_extractor: np.sqrt(2), self.mlp_extractor: np.sqrt(2), self.action_net: 0.01, self.value_net: 1, } for module, gain in module_gains.items(): module.apply(partial(self.init_weights, gain=gain)) # Setup optimizer with initial learning rate self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
[docs] def forward(self, obs: th.Tensor, deterministic: bool = False) -> Tuple[th.Tensor, th.Tensor, th.Tensor]: """ Forward pass in all the networks (actor and critic) :param obs: Observation :param deterministic: Whether to sample or use deterministic actions :return: action, value and log probability of the action """ # Preprocess the observation if needed features = self.extract_features(obs) latent_pi, latent_vf = self.mlp_extractor(features) # Evaluate the values for the given observations values = self.value_net(latent_vf) distribution = self._get_action_dist_from_latent(latent_pi) actions = distribution.get_actions(deterministic=deterministic) log_prob = distribution.log_prob(actions) actions = actions.reshape((-1,) + self.action_space.shape) return actions, values, log_prob
def _get_action_dist_from_latent(self, latent_pi: th.Tensor) -> Distribution: """ Retrieve action distribution given the latent codes. :param latent_pi: Latent code for the actor :return: Action distribution """ mean_actions = self.action_net(latent_pi) if isinstance(self.action_dist, DiagGaussianDistribution): return self.action_dist.proba_distribution(mean_actions, self.log_std) elif isinstance(self.action_dist, CategoricalDistribution): # Here mean_actions are the logits before the softmax return self.action_dist.proba_distribution(action_logits=mean_actions) elif isinstance(self.action_dist, MultiCategoricalDistribution): # Here mean_actions are the flattened logits return self.action_dist.proba_distribution(action_logits=mean_actions) elif isinstance(self.action_dist, BernoulliDistribution): # Here mean_actions are the logits (before rounding to get the binary actions) return self.action_dist.proba_distribution(action_logits=mean_actions) elif isinstance(self.action_dist, StateDependentNoiseDistribution): return self.action_dist.proba_distribution(mean_actions, self.log_std, latent_pi) else: raise ValueError("Invalid action distribution") def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor: """ Get the action according to the policy for a given observation. :param observation: :param deterministic: Whether to use stochastic or deterministic actions :return: Taken action according to the policy """ return self.get_distribution(observation).get_actions(deterministic=deterministic)
[docs] def evaluate_actions(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, th.Tensor, Optional[th.Tensor]]: """ Evaluate actions according to the current policy, given the observations. :param obs: :param actions: :return: estimated value, log likelihood of taking those actions and entropy of the action distribution. """ # Preprocess the observation if needed features = self.extract_features(obs) latent_pi, latent_vf = self.mlp_extractor(features) distribution = self._get_action_dist_from_latent(latent_pi) log_prob = distribution.log_prob(actions) values = self.value_net(latent_vf) entropy = distribution.entropy() return values, log_prob, entropy
[docs] def get_distribution(self, obs: th.Tensor) -> Distribution: """ Get the current policy distribution given the observations. :param obs: :return: the action distribution. """ features = self.extract_features(obs) latent_pi = self.mlp_extractor.forward_actor(features) return self._get_action_dist_from_latent(latent_pi)
[docs] def predict_values(self, obs: th.Tensor) -> th.Tensor: """ Get the estimated values according to the current policy given the observations. :param obs: :return: the estimated values. """ features = self.extract_features(obs) latent_vf = self.mlp_extractor.forward_critic(features) return self.value_net(latent_vf)
[docs]class ActorCriticCnnPolicy(ActorCriticPolicy): """ CNN policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes. :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 ortho_init: Whether to use or not orthogonal initialization :param use_sde: Whether to use State Dependent Exploration or not :param log_std_init: Initial value for the log standard deviation :param full_std: Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDE :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough. :param squash_output: Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE. :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 """ def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None, activation_fn: Type[nn.Module] = nn.Tanh, ortho_init: bool = True, use_sde: bool = False, log_std_init: float = 0.0, full_std: bool = True, use_expln: bool = False, squash_output: bool = False, 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().__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, ortho_init, use_sde, log_std_init, full_std, use_expln, squash_output, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, )
[docs]class MultiInputActorCriticPolicy(ActorCriticPolicy): """ MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes. :param observation_space: Observation space (Tuple) :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 ortho_init: Whether to use or not orthogonal initialization :param use_sde: Whether to use State Dependent Exploration or not :param log_std_init: Initial value for the log standard deviation :param full_std: Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDE :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough. :param squash_output: Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE. :param features_extractor_class: Uses the CombinedExtractor :param features_extractor_kwargs: Keyword arguments to pass to the feature 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 """ def __init__( self, observation_space: gym.spaces.Dict, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None, activation_fn: Type[nn.Module] = nn.Tanh, ortho_init: bool = True, use_sde: bool = False, log_std_init: float = 0.0, full_std: bool = True, use_expln: bool = False, squash_output: bool = False, 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, ): super().__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, ortho_init, use_sde, log_std_init, full_std, use_expln, squash_output, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, )
class ContinuousCritic(BaseModel): """ Critic network(s) for DDPG/SAC/TD3. It represents the action-state value function (Q-value function). Compared to A2C/PPO critics, this one represents the Q-value and takes the continuous action as input. It is concatenated with the state and then fed to the network which outputs a single value: Q(s, a). For more recent algorithms like SAC/TD3, multiple networks are created to give different estimates. By default, it creates two critic networks used to reduce overestimation thanks to clipped Q-learning (cf TD3 paper). :param observation_space: Obervation 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) :param n_critics: Number of critic networks to create. :param share_features_extractor: Whether the features extractor is shared or not between the actor and the critic (this saves computation time) """ def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, net_arch: List[int], features_extractor: nn.Module, features_dim: int, activation_fn: Type[nn.Module] = nn.ReLU, normalize_images: bool = True, n_critics: int = 2, share_features_extractor: bool = True, ): super().__init__( observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images, ) action_dim = get_action_dim(self.action_space) self.share_features_extractor = share_features_extractor self.n_critics = n_critics self.q_networks = [] for idx in range(n_critics): q_net = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn) q_net = nn.Sequential(*q_net) self.add_module(f"qf{idx}", q_net) self.q_networks.append(q_net) def forward(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, ...]: # Learn the features extractor using the policy loss only # when the features_extractor is shared with the actor with th.set_grad_enabled(not self.share_features_extractor): features = self.extract_features(obs) qvalue_input = th.cat([features, actions], dim=1) return tuple(q_net(qvalue_input) for q_net in self.q_networks) def q1_forward(self, obs: th.Tensor, actions: th.Tensor) -> th.Tensor: """ Only predict the Q-value using the first network. This allows to reduce computation when all the estimates are not needed (e.g. when updating the policy in TD3). """ with th.no_grad(): features = self.extract_features(obs) return self.q_networks[0](th.cat([features, actions], dim=1))