575 lines
23 KiB
Python
575 lines
23 KiB
Python
import warnings
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from typing import Any, Dict, List, Optional, Tuple, Type, Union
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import math
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import gym
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import torch as th
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from torch import nn
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from stable_baselines3.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
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from stable_baselines3.common.policies import BasePolicy, ContinuousCritic
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from stable_baselines3.common.preprocessing import get_action_dim
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from stable_baselines3.common.torch_layers import (
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BaseFeaturesExtractor,
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CombinedExtractor,
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FlattenExtractor,
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NatureCNN,
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create_mlp,
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get_actor_critic_arch,
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)
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from stable_baselines3.common.type_aliases import Schedule
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from ..distributions import UniversalGaussianDistribution
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# CAP the standard deviation of the actor
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CHOL_MIN = 0.001
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CHOL_MAX = 1000
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class Actor(BasePolicy):
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"""
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Actor network (policy) for SAC.
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:param observation_space: Obervation space
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:param action_space: Action space
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:param net_arch: Network architecture
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:param features_extractor: Network to extract features
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(a CNN when using images, a nn.Flatten() layer otherwise)
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:param features_dim: Number of features
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:param activation_fn: Activation function
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:param use_sde: Whether to use State Dependent Exploration or not
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:param log_std_init: Initial value for the log standard deviation
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:param full_std: Whether to use (n_features x n_actions) parameters
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for the std instead of only (n_features,) when using gSDE.
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:param sde_net_arch: Network architecture for extracting features
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when using gSDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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net_arch: List[int],
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features_extractor: nn.Module,
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features_dim: int,
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activation_fn: Type[nn.Module] = nn.ReLU,
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use_sde: bool = False,
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use_pca: bool = False,
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log_std_init: float = -3,
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full_std: bool = True,
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sde_net_arch: Optional[List[int]] = None,
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use_expln: bool = False,
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clip_mean: float = 2.0,
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normalize_images: bool = True,
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dist_kwargs={},
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latent_dim_sde=None,
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):
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super().__init__(
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observation_space,
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action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images,
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squash_output=True,
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)
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assert use_pca == False, 'PCA is not implemented for SAC'
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# Save arguments to re-create object at loading
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self.use_sde = use_sde
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self.sde_features_extractor = None
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self.net_arch = net_arch
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self.features_dim = features_dim
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self.activation_fn = activation_fn
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self.log_std_init = log_std_init
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self.sde_net_arch = sde_net_arch
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self.use_expln = use_expln
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self.full_std = full_std
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self.clip_mean = clip_mean
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self.latent_dim_sde = latent_dim_sde
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if sde_net_arch is not None:
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warnings.warn(
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"sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning)
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action_dim = get_action_dim(self.action_space)
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latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn)
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self.latent_pi = nn.Sequential(*latent_pi_net)
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last_layer_dim = net_arch[-1] if len(net_arch) > 0 else features_dim
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if self.use_sde:
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add_dist_kwargs = {
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'use_sde': True,
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# "use_expln": use_expln,
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# "learn_features": False,
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}
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for k in add_dist_kwargs:
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dist_kwargs[k] = add_dist_kwargs[k]
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self.action_dist = UniversalGaussianDistribution(
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action_dim, **dist_kwargs)
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self.mu_net, self.chol_net = self.action_dist.proba_distribution_net(
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latent_dim=last_layer_dim, latent_sde_dim=self.latent_dim_sde or last_layer_dim, std_init=math.exp(
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self.log_std_init)
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)
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# self.action_dist = StateDependentNoiseDistribution(
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# action_dim, full_std=full_std, use_expln=use_expln, learn_features=True, squash_output=True
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# )
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# self.mu_net, self.chol_net = self.action_dist.proba_distribution_net(
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# latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, log_std_init=log_std_init
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# )
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# Avoid numerical issues by limiting the mean of the Gaussian
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# to be in [-clip_mean, clip_mean]
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# if clip_mean > 0.0:
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# self.mu = nn.Sequential(self.mu, nn.Hardtanh(
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# min_val=-clip_mean, max_val=clip_mean))
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else:
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self.action_dist = UniversalGaussianDistribution(
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action_dim, **dist_kwargs)
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self.mu_net, self.chol_net = self.action_dist.proba_distribution_net(
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latent_dim=last_layer_dim, latent_sde_dim=last_layer_dim, std_init=math.exp(
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self.log_std_init)
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)
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# self.action_dist = SquashedDiagGaussianDistribution(action_dim)
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# self.mu = nn.Linear(last_layer_dim, action_dim)
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# self.log_std = nn.Linear(last_layer_dim, action_dim)
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def _get_constructor_parameters(self) -> Dict[str, Any]:
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data = super()._get_constructor_parameters()
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data.update(
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dict(
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net_arch=self.net_arch,
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features_dim=self.features_dim,
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activation_fn=self.activation_fn,
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use_sde=self.use_sde,
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log_std_init=self.log_std_init,
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full_std=self.full_std,
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use_expln=self.use_expln,
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features_extractor=self.features_extractor,
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clip_mean=self.clip_mean,
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)
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)
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return data
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def get_std(self) -> th.Tensor:
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"""
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Retrieve the standard deviation of the action distribution.
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Only useful when using gSDE.
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It corresponds to ``th.exp(log_std)`` in the normal case,
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but is slightly different when using ``expln`` function
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(cf StateDependentNoiseDistribution doc).
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:return:
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"""
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msg = "get_std() is only available when using gSDE"
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assert isinstance(self.action_dist,
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StateDependentNoiseDistribution) or (isinstance(self.action_dist, UniversalGaussianDistribution) and self.action_dist.use_sde), msg
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return self.chol
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def reset_noise(self, n_envs: int = 1) -> None:
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"""
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Sample new weights for the exploration matrix.
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:param n_envs:
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"""
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assert isinstance(
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self.action_dist, StateDependentNoiseDistribution) or isinstance(
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self.action_dist, UniversalGaussianDistribution), "reset_noise() is only available when using gSDE"
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if isinstance(
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self.action_dist, StateDependentNoiseDistribution):
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self.action_dist.sample_weights(self.chol, batch_size=n_envs)
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if isinstance(
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self.action_dist, UniversalGaussianDistribution):
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self.action_dist.sample_weights(batch_size=n_envs)
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def get_action_dist_params(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]:
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"""
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Get the parameters for the action distribution.
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:param obs:
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:return:
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Mean, standard deviation and optional keyword arguments.
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"""
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features = self.extract_features(obs)
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latent_pi = self.latent_pi(features)
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mean_actions = self.mu_net(latent_pi)
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# Unstructured exploration (Original implementation)
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chol = self.chol_net(latent_pi)
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self.chol = chol
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# Original Implementation to cap the standard deviation
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self.chol = th.clamp(chol, CHOL_MIN, CHOL_MAX)
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if self.use_sde:
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return mean_actions, self.chol, dict(latent_sde=latent_pi)
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return mean_actions, self.chol, {}
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def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
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mean_actions, chol, kwargs = self.get_action_dist_params(obs)
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# Note: the action is squashed
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return self.action_dist.actions_from_params(mean_actions, chol, deterministic=deterministic, **kwargs)
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def action_log_prob(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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mean_actions, chol, kwargs = self.get_action_dist_params(obs)
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# return action and associated log prob
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return self.action_dist.log_prob_from_params(mean_actions, chol, **kwargs)
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self(observation, deterministic)
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class SACPolicy(BasePolicy):
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"""
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Policy class (with both actor and critic) for SAC.
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:param observation_space: Observation space
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:param action_space: Action space
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:param lr_schedule: Learning rate schedule (could be constant)
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param use_sde: Whether to use State Dependent Exploration or not
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:param log_std_init: Initial value for the log standard deviation
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:param sde_net_arch: Network architecture for extracting features
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when using gSDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
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:param features_extractor_class: Features extractor to use.
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:param features_extractor_kwargs: Keyword arguments
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to pass to the features extractor.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: Additional keyword arguments,
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excluding the learning rate, to pass to the optimizer
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:param n_critics: Number of critic networks to create.
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:param share_features_extractor: Whether to share or not the features extractor
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between the actor and the critic (this saves computation time)
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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lr_schedule: Schedule,
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net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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use_sde: bool = False,
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std_init: float = 0.05,
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sde_net_arch: Optional[List[int]] = None,
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use_expln: bool = False,
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clip_mean: float = 2.0,
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features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
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features_extractor_kwargs: Optional[Dict[str, Any]] = None,
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normalize_images: bool = True,
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optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
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optimizer_kwargs: Optional[Dict[str, Any]] = None,
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n_critics: int = 2,
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share_features_extractor: bool = True,
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dist_kwargs={},
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latent_dim_sde=None,
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):
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super().__init__(
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observation_space,
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action_space,
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features_extractor_class,
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features_extractor_kwargs,
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optimizer_class=optimizer_class,
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optimizer_kwargs=optimizer_kwargs,
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squash_output=True,
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)
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if net_arch is None:
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if features_extractor_class == NatureCNN:
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net_arch = []
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else:
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net_arch = [256, 256]
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actor_arch, critic_arch = get_actor_critic_arch(net_arch)
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self.net_arch = net_arch
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self.activation_fn = activation_fn
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self.net_args = {
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"observation_space": self.observation_space,
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"action_space": self.action_space,
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"net_arch": actor_arch,
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"activation_fn": self.activation_fn,
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"normalize_images": normalize_images,
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}
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self.actor_kwargs = self.net_args.copy()
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if sde_net_arch is not None:
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warnings.warn(
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"sde_net_arch is deprecated and will be removed in SB3 v2.4.0.", DeprecationWarning)
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sde_kwargs = {
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"use_sde": use_sde,
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"log_std_init": math.log(std_init),
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"use_expln": use_expln,
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"clip_mean": clip_mean,
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}
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self.actor_kwargs.update(sde_kwargs)
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self.actor_kwargs.update({'latent_dim_sde': latent_dim_sde})
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self.critic_kwargs = self.net_args.copy()
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self.critic_kwargs.update(
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{
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"n_critics": n_critics,
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"net_arch": critic_arch,
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"share_features_extractor": share_features_extractor,
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}
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)
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self.actor, self.actor_target = None, None
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self.critic, self.critic_target = None, None
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self.share_features_extractor = share_features_extractor
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self.dist_kwargs = dist_kwargs
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self._build(lr_schedule)
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def _build(self, lr_schedule: Schedule) -> None:
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self.actor = self.make_actor()
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self.actor.optimizer = self.optimizer_class(
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self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
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if self.share_features_extractor:
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self.critic = self.make_critic(
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features_extractor=self.actor.features_extractor)
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# Do not optimize the shared features extractor with the critic loss
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# otherwise, there are gradient computation issues
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critic_parameters = [param for name, param in self.critic.named_parameters(
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) if "features_extractor" not in name]
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else:
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# Create a separate features extractor for the critic
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# this requires more memory and computation
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self.critic = self.make_critic(features_extractor=None)
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critic_parameters = self.critic.parameters()
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# Critic target should not share the features extractor with critic
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self.critic_target = self.make_critic(features_extractor=None)
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self.critic_target.load_state_dict(self.critic.state_dict())
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self.critic.optimizer = self.optimizer_class(
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critic_parameters, lr=lr_schedule(1), **self.optimizer_kwargs)
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# Target networks should always be in eval mode
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self.critic_target.set_training_mode(False)
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def _get_constructor_parameters(self) -> Dict[str, Any]:
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data = super()._get_constructor_parameters()
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data.update(
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dict(
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net_arch=self.net_arch,
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activation_fn=self.net_args["activation_fn"],
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use_sde=self.actor_kwargs["use_sde"],
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log_std_init=self.actor_kwargs["log_std_init"],
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use_expln=self.actor_kwargs["use_expln"],
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clip_mean=self.actor_kwargs["clip_mean"],
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n_critics=self.critic_kwargs["n_critics"],
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# dummy lr schedule, not needed for loading policy alone
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lr_schedule=self._dummy_schedule,
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optimizer_class=self.optimizer_class,
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optimizer_kwargs=self.optimizer_kwargs,
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features_extractor_class=self.features_extractor_class,
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features_extractor_kwargs=self.features_extractor_kwargs,
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)
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)
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return data
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def reset_noise(self, batch_size: int = 1) -> None:
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"""
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Sample new weights for the exploration matrix, when using gSDE.
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:param batch_size:
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"""
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# self.actor.reset_noise(batch_size=batch_size)
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self.actor.reset_noise()
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def make_actor(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> Actor:
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actor_kwargs = self._update_features_extractor(
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self.actor_kwargs, features_extractor)
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return Actor(**actor_kwargs, dist_kwargs=self.dist_kwargs).to(self.device)
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def make_critic(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> ContinuousCritic:
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critic_kwargs = self._update_features_extractor(
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self.critic_kwargs, features_extractor)
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return ContinuousCritic(**critic_kwargs).to(self.device)
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def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self._predict(obs, deterministic=deterministic)
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self.actor(observation, deterministic)
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def set_training_mode(self, mode: bool) -> None:
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"""
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Put the policy in either training or evaluation mode.
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This affects certain modules, such as batch normalisation and dropout.
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:param mode: if true, set to training mode, else set to evaluation mode
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"""
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self.actor.set_training_mode(mode)
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self.critic.set_training_mode(mode)
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self.training = mode
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MlpPolicy = SACPolicy
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class CnnPolicy(SACPolicy):
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"""
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Policy class (with both actor and critic) for SAC.
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:param observation_space: Observation space
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:param action_space: Action space
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|
:param lr_schedule: Learning rate schedule (could be constant)
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param use_sde: Whether to use State Dependent Exploration or not
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:param log_std_init: Initial value for the log standard deviation
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:param sde_net_arch: Network architecture for extracting features
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when using gSDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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|
:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
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:param features_extractor_class: Features extractor to use.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: Additional keyword arguments,
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excluding the learning rate, to pass to the optimizer
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|
: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,
|
|
)
|