mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-09-04 20:23:54 +00:00
44f8218df0
* Split torch module code into torch_layers file * Updated reference to CNN * Change 'CxWxH' to 'CxHxW', as per common notion * Fix missing import in policies.py * Move PPOPolicy to OnlineActorCriticPolicy * Create OnPolicyRLModel from PPO, and make A2C and PPO inherit * Update A2C optimizer comment * Clean weight init scales for clarity * Fix A2C log_interval default parameter * Rename 'progress' to 'progress_remaining * Rename 'Models' to 'Algorithms' * Rename 'OnlineActorCriticPolicy' to 'ActorCriticPolicy' * Move static functions out from BaseAlgorithm * Move on/off_policy base algorithms to their own files * Add files for A2C/PPO * Fix docs * Fix pytype * Update documentation on OnPolicyAlgorithm * Add proper doctstring for on_policy rollout gathering * Add bit clarification on the mlppolicy/cnnpolicy naming * Move static function is_vectorized_policies to utils.py * Checking docstrings, pep8 fixes * Update changelog * Clean changelog * Remove policy warnings for sac/td3 * Add monitor_wrapper for OnPolicyAlgorithm. Clean tb logging variables. Add parameter keywords to OffPolicyAlgorithm super init Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
281 lines
13 KiB
Python
281 lines
13 KiB
Python
from typing import Optional, List, Tuple, Callable, Union, Type, Any, Dict
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import gym
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import torch as th
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import torch.nn as nn
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from stable_baselines3.common.preprocessing import get_action_dim
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from stable_baselines3.common.policies import BasePolicy, register_policy
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from stable_baselines3.common.torch_layers import create_mlp, NatureCNN, BaseFeaturesExtractor, FlattenExtractor
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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: (gym.spaces.Space) Obervation space
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:param action_space: (gym.spaces.Space) Action space
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:param net_arch: ([int]) Network architecture
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:param features_extractor: (nn.Module) 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: (int) Number of features
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:param activation_fn: (Type[nn.Module]) Activation function
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param device: (Union[th.device, str]) Device on which the code should run.
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"""
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def __init__(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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device: Union[th.device, str] = 'auto'):
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super(Actor, self).__init__(observation_space, action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images,
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device=device,
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squash_output=True)
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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(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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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 Critic(BasePolicy):
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"""
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Critic network for TD3,
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in fact it represents the action-state value function (Q-value function)
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:param observation_space: (gym.spaces.Space) Obervation space
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:param action_space: (gym.spaces.Space) Action space
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:param net_arch: ([int]) Network architecture
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:param features_extractor: (nn.Module) 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: (int) Number of features
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:param activation_fn: (Type[nn.Module]) Activation function
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param device: (Union[th.device, str]) Device on which the code should run.
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"""
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def __init__(self, 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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device: Union[th.device, str] = 'auto'):
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super(Critic, self).__init__(observation_space, action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images,
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device=device)
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action_dim = get_action_dim(self.action_space)
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q1_net = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn)
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self.q1_net = nn.Sequential(*q1_net)
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q2_net = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn)
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self.q2_net = nn.Sequential(*q2_net)
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def forward(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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# Learn the features extractor using the policy loss only
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with th.no_grad():
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features = self.extract_features(obs)
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qvalue_input = th.cat([features, actions], dim=1)
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return self.q1_net(qvalue_input), self.q2_net(qvalue_input)
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def q1_forward(self, obs: th.Tensor, actions: th.Tensor) -> th.Tensor:
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with th.no_grad():
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features = self.extract_features(obs)
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return self.q1_net(th.cat([features, actions], dim=1))
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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: (gym.spaces.Space) Observation space
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:param action_space: (gym.spaces.Space) Action space
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:param lr_schedule: (Callable) Learning rate schedule (could be constant)
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:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
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:param device: (Union[th.device, str]) Device on which the code should run.
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:param activation_fn: (Type[nn.Module]) Activation function
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:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
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:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
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to pass to the feature extractor.
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
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excluding the learning rate, to pass to the optimizer
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"""
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def __init__(self, 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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device: Union[th.device, str] = 'auto',
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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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super(TD3Policy, self).__init__(observation_space, action_space,
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device,
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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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# 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,
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**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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'device': device
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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._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),
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**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),
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**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(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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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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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) -> Critic:
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return Critic(**self.net_args).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: (gym.spaces.Space) Observation space
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:param action_space: (gym.spaces.Space) Action space
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:param lr_schedule: (Callable) Learning rate schedule (could be constant)
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:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
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:param device: (Union[th.device, str]) Device on which the code should run.
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:param activation_fn: (Type[nn.Module]) Activation function
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:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
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:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
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to pass to the feature extractor.
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
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excluding the learning rate, to pass to the optimizer
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"""
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def __init__(self, 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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device: Union[th.device, str] = 'auto',
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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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super(CnnPolicy, self).__init__(observation_space,
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action_space,
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lr_schedule,
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net_arch,
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device,
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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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register_policy("MlpPolicy", MlpPolicy)
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register_policy("CnnPolicy", CnnPolicy)
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