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https://github.com/saymrwulf/stable-baselines3.git
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55912576ed
* Cleanup docstring types * Update style * Test with js hack * Revert "Test with js hack" This reverts commit d091f438e8851ab8d01b66628e06a104f5e5ec69. * Fix types * Fix typo * Update CONTRIBUTING example
245 lines
9.2 KiB
Python
245 lines
9.2 KiB
Python
from typing import Any, Callable, Dict, List, Optional, Type
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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.policies import BasePolicy, ContinuousCritic, register_policy
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from stable_baselines3.common.preprocessing import get_action_dim
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from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
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class Actor(BasePolicy):
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"""
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Actor network (policy) for TD3.
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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 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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normalize_images: bool = True,
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):
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super(Actor, self).__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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self.features_extractor = features_extractor
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self.normalize_images = normalize_images
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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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action_dim = get_action_dim(self.action_space)
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actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True)
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# Deterministic action
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self.mu = nn.Sequential(*actor_net)
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def _get_data(self) -> Dict[str, Any]:
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data = super()._get_data()
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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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features_extractor=self.features_extractor,
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)
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)
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return data
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def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
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# assert deterministic, 'The TD3 actor only outputs deterministic actions'
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features = self.extract_features(obs)
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return self.mu(features)
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self.forward(observation, deterministic=deterministic)
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class TD3Policy(BasePolicy):
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"""
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Policy class (with both actor and critic) for TD3.
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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 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 feature 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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"""
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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: Callable,
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net_arch: Optional[List[int]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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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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):
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super(TD3Policy, self).__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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# Default network architecture, from the original paper
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if net_arch is None:
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if features_extractor_class == FlattenExtractor:
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net_arch = [400, 300]
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else:
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net_arch = []
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self.features_extractor = features_extractor_class(self.observation_space, **self.features_extractor_kwargs)
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self.features_dim = self.features_extractor.features_dim
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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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"features_extractor": self.features_extractor,
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"features_dim": self.features_dim,
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"net_arch": self.net_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.critic_kwargs = self.net_args.copy()
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self.critic_kwargs.update({"n_critics": n_critics})
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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._build(lr_schedule)
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def _build(self, lr_schedule: Callable) -> None:
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self.actor = self.make_actor()
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self.actor_target = self.make_actor()
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self.actor_target.load_state_dict(self.actor.state_dict())
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self.actor.optimizer = self.optimizer_class(self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
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self.critic = self.make_critic()
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self.critic_target = self.make_critic()
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self.critic_target.load_state_dict(self.critic.state_dict())
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self.critic.optimizer = self.optimizer_class(self.critic.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
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def _get_data(self) -> Dict[str, Any]:
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data = super()._get_data()
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data.update(
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dict(
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net_arch=self.net_args["net_arch"],
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activation_fn=self.net_args["activation_fn"],
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n_critics=self.critic_kwargs["n_critics"],
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lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
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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 make_actor(self) -> Actor:
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return Actor(**self.net_args).to(self.device)
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def make_critic(self) -> ContinuousCritic:
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return ContinuousCritic(**self.critic_kwargs).to(self.device)
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def forward(self, observation: th.Tensor, deterministic: bool = False):
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return self._predict(observation, 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=deterministic)
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MlpPolicy = TD3Policy
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class CnnPolicy(TD3Policy):
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"""
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Policy class (with both actor and critic) for TD3.
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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 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 feature 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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"""
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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: Callable,
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net_arch: Optional[List[int]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
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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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):
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super(CnnPolicy, self).__init__(
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observation_space,
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action_space,
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lr_schedule,
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net_arch,
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activation_fn,
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features_extractor_class,
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features_extractor_kwargs,
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normalize_images,
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optimizer_class,
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optimizer_kwargs,
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n_critics,
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)
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register_policy("MlpPolicy", MlpPolicy)
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register_policy("CnnPolicy", CnnPolicy)
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