import warnings from typing import Any, Dict, List, Optional, Tuple, Type, Union import math import gym import torch as th from torch import nn from stable_baselines3.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution 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 Schedule from ..distributions import UniversalGaussianDistribution # CAP the standard deviation of the actor LOG_STD_MAX = 2 LOG_STD_MIN = -20 class Actor(BasePolicy): """ Actor network (policy) for SAC. :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 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 sde_net_arch: Network architecture for extracting features when using gSDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability. :param normalize_images: 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, net_arch: List[int], features_extractor: nn.Module, features_dim: int, activation_fn: Type[nn.Module] = nn.ReLU, use_sde: bool = False, log_std_init: float = -3, full_std: bool = True, sde_net_arch: Optional[List[int]] = None, use_expln: bool = False, clip_mean: float = 2.0, normalize_images: bool = True, dist_kwargs={}, ): super().__init__( observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images, squash_output=True, ) # Save arguments to re-create object at loading self.use_sde = use_sde self.sde_features_extractor = None self.net_arch = net_arch self.features_dim = features_dim self.activation_fn = activation_fn self.log_std_init = log_std_init self.sde_net_arch = sde_net_arch self.use_expln = use_expln self.full_std = full_std self.clip_mean = clip_mean if sde_net_arch is not None: warnings.warn( "sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning) action_dim = get_action_dim(self.action_space) latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn) self.latent_pi = nn.Sequential(*latent_pi_net) last_layer_dim = net_arch[-1] if len(net_arch) > 0 else features_dim if self.use_sde: add_dist_kwargs = { 'use_sde': True, # "use_expln": use_expln, # "learn_features": False, } for k in add_dist_kwargs: dist_kwargs[k] = add_dist_kwargs[k] self.action_dist = UniversalGaussianDistribution( action_dim, **dist_kwargs) self.mu_net, self.chol_net = self.action_dist.proba_distribution_net( latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, std_init=math.exp( self.log_std_init) ) # self.action_dist = StateDependentNoiseDistribution( # action_dim, full_std=full_std, use_expln=use_expln, learn_features=True, squash_output=True # ) # self.mu_net, self.chol_net = self.action_dist.proba_distribution_net( # latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, log_std_init=log_std_init # ) # Avoid numerical issues by limiting the mean of the Gaussian # to be in [-clip_mean, clip_mean] # if clip_mean > 0.0: # self.mu = nn.Sequential(self.mu, nn.Hardtanh( # min_val=-clip_mean, max_val=clip_mean)) else: self.action_dist = UniversalGaussianDistribution( action_dim, **dist_kwargs) self.mu_net, self.chol_net = self.action_dist.proba_distribution_net( latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, std_init=math.exp( self.log_std_init) ) # self.action_dist = SquashedDiagGaussianDistribution(action_dim) # self.mu = nn.Linear(last_layer_dim, action_dim) # self.log_std = nn.Linear(last_layer_dim, action_dim) 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, use_sde=self.use_sde, log_std_init=self.log_std_init, full_std=self.full_std, use_expln=self.use_expln, features_extractor=self.features_extractor, clip_mean=self.clip_mean, ) ) return data def get_std(self) -> th.Tensor: """ Retrieve the standard deviation of the action distribution. Only useful when using gSDE. It corresponds to ``th.exp(log_std)`` in the normal case, but is slightly different when using ``expln`` function (cf StateDependentNoiseDistribution doc). :return: """ msg = "get_std() is only available when using gSDE" assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg return self.chol 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) or isinstance( self.action_dist, UniversalGaussianDistribution), "reset_noise() is only available when using gSDE" if isinstance( self.action_dist, StateDependentNoiseDistribution): self.action_dist.sample_weights(self.chol, batch_size=n_envs) if isinstance( self.action_dist, UniversalGaussianDistribution): self.action_dist.sample_weights(batch_size=n_envs) def get_action_dist_params(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]: """ Get the parameters for the action distribution. :param obs: :return: Mean, standard deviation and optional keyword arguments. """ features = self.extract_features(obs) latent_pi = self.latent_pi(features) mean_actions = self.mu_net(latent_pi) if self.use_sde: return mean_actions, self.chol, dict(latent_sde=latent_pi) # Unstructured exploration (Original implementation) chol = self.chol_net(latent_pi) # Original Implementation to cap the standard deviation self.chol = th.clamp(chol, LOG_STD_MIN, LOG_STD_MAX) return mean_actions, self.chol, {} def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor: mean_actions, chol, kwargs = self.get_action_dist_params(obs) # Note: the action is squashed return self.action_dist.actions_from_params(mean_actions, chol, deterministic=deterministic, **kwargs) def action_log_prob(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]: mean_actions, chol, kwargs = self.get_action_dist_params(obs) # return action and associated log prob return self.action_dist.log_prob_from_params(mean_actions, chol, **kwargs) def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor: return self(observation, deterministic) class SACPolicy(BasePolicy): """ Policy class (with both actor and critic) for SAC. :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 use_sde: Whether to use State Dependent Exploration or not :param log_std_init: Initial value for the log standard deviation :param sde_net_arch: Network architecture for extracting features when using gSDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability. :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: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None, activation_fn: Type[nn.Module] = nn.ReLU, use_sde: bool = False, log_std_init: float = -3, sde_net_arch: Optional[List[int]] = None, use_expln: bool = False, clip_mean: float = 2.0, 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 = True, dist_kwargs={}, ): super().__init__( observation_space, action_space, features_extractor_class, features_extractor_kwargs, optimizer_class=optimizer_class, optimizer_kwargs=optimizer_kwargs, squash_output=True, ) if net_arch is None: if features_extractor_class == NatureCNN: net_arch = [] else: net_arch = [256, 256] 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() if sde_net_arch is not None: warnings.warn( "sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning) sde_kwargs = { "use_sde": use_sde, "log_std_init": log_std_init, "use_expln": use_expln, "clip_mean": clip_mean, } self.actor_kwargs.update(sde_kwargs) 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.actor, self.actor_target = None, None self.critic, self.critic_target = None, None self.share_features_extractor = share_features_extractor self.dist_kwargs = dist_kwargs self._build(lr_schedule) def _build(self, lr_schedule: Schedule) -> None: self.actor = self.make_actor() self.actor.optimizer = self.optimizer_class( self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs) if self.share_features_extractor: self.critic = self.make_critic( features_extractor=self.actor.features_extractor) # Do not optimize the shared features extractor with the critic loss # otherwise, there are gradient computation issues critic_parameters = [param for name, param in self.critic.named_parameters( ) if "features_extractor" not in name] else: # Create a separate features extractor for the critic # this requires more memory and computation self.critic = self.make_critic(features_extractor=None) critic_parameters = self.critic.parameters() # Critic target should not share the features extractor with critic 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( critic_parameters, lr=lr_schedule(1), **self.optimizer_kwargs) # Target networks should always be in eval mode 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"], use_sde=self.actor_kwargs["use_sde"], log_std_init=self.actor_kwargs["log_std_init"], use_expln=self.actor_kwargs["use_expln"], clip_mean=self.actor_kwargs["clip_mean"], n_critics=self.critic_kwargs["n_critics"], # dummy lr schedule, not needed for loading policy alone lr_schedule=self._dummy_schedule, 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 reset_noise(self, batch_size: int = 1) -> None: """ Sample new weights for the exploration matrix, when using gSDE. :param batch_size: """ self.actor.reset_noise(batch_size=batch_size) 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, dist_kwargs=self.dist_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, obs: th.Tensor, deterministic: bool = False) -> th.Tensor: return self._predict(obs, deterministic=deterministic) def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor: return self.actor(observation, deterministic) 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 = SACPolicy class CnnPolicy(SACPolicy): """ Policy class (with both actor and critic) for SAC. :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 use_sde: Whether to use State Dependent Exploration or not :param log_std_init: Initial value for the log standard deviation :param sde_net_arch: Network architecture for extracting features when using gSDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability. :param features_extractor_class: Features extractor to use. :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: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None, activation_fn: Type[nn.Module] = nn.ReLU, use_sde: bool = False, log_std_init: float = -3, sde_net_arch: Optional[List[int]] = None, use_expln: bool = False, clip_mean: float = 2.0, 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 = True, ): super().__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, use_sde, log_std_init, sde_net_arch, use_expln, clip_mean, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, n_critics, share_features_extractor, ) class MultiInputPolicy(SACPolicy): """ Policy class (with both actor and critic) for SAC. :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 use_sde: Whether to use State Dependent Exploration or not :param log_std_init: Initial value for the log standard deviation :param sde_net_arch: Network architecture for extracting features when using gSDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability. :param features_extractor_class: Features extractor to use. :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: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None, activation_fn: Type[nn.Module] = nn.ReLU, use_sde: bool = False, log_std_init: float = -3, sde_net_arch: Optional[List[int]] = None, use_expln: bool = False, clip_mean: float = 2.0, 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 = True, ): super().__init__( observation_space, action_space, lr_schedule, net_arch, activation_fn, use_sde, log_std_init, sde_net_arch, use_expln, clip_mean, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, n_critics, share_features_extractor, )