from typing import Optional, List, Tuple, Callable, Union, Type, Any, Dict import gym import torch as th import torch.nn as nn from stable_baselines3.common.preprocessing import get_action_dim from stable_baselines3.common.policies import BasePolicy, register_policy from stable_baselines3.common.torch_layers import create_mlp, NatureCNN, BaseFeaturesExtractor, FlattenExtractor class Actor(BasePolicy): """ Actor network (policy) for TD3. :param observation_space: (gym.spaces.Space) Obervation space :param action_space: (gym.spaces.Space) Action space :param net_arch: ([int]) Network architecture :param features_extractor: (nn.Module) Network to extract features (a CNN when using images, a nn.Flatten() layer otherwise) :param features_dim: (int) Number of features :param activation_fn: (Type[nn.Module]) Activation function :param normalize_images: (bool) Whether to normalize images or not, dividing by 255.0 (True by default) :param device: (Union[th.device, str]) Device on which the code should run. """ def __init__(self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, net_arch: List[int], features_extractor: nn.Module, features_dim: int, activation_fn: Type[nn.Module] = nn.ReLU, normalize_images: bool = True, device: Union[th.device, str] = 'auto'): super(Actor, self).__init__(observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images, device=device, squash_output=True) self.features_extractor = features_extractor self.normalize_images = normalize_images self.net_arch = net_arch self.features_dim = features_dim self.activation_fn = activation_fn action_dim = get_action_dim(self.action_space) actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True) # Deterministic action self.mu = nn.Sequential(*actor_net) def _get_data(self) -> Dict[str, Any]: data = super()._get_data() data.update(dict( net_arch=self.net_arch, features_dim=self.features_dim, activation_fn=self.activation_fn, features_extractor=self.features_extractor )) return data def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor: # assert deterministic, 'The TD3 actor only outputs deterministic actions' features = self.extract_features(obs) return self.mu(features) def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor: return self.forward(observation, deterministic=deterministic) class Critic(BasePolicy): """ Critic network for TD3, in fact it represents the action-state value function (Q-value function) :param observation_space: (gym.spaces.Space) Obervation space :param action_space: (gym.spaces.Space) Action space :param net_arch: ([int]) Network architecture :param features_extractor: (nn.Module) Network to extract features (a CNN when using images, a nn.Flatten() layer otherwise) :param features_dim: (int) Number of features :param activation_fn: (Type[nn.Module]) Activation function :param normalize_images: (bool) Whether to normalize images or not, dividing by 255.0 (True by default) :param device: (Union[th.device, str]) Device on which the code should run. """ def __init__(self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, net_arch: List[int], features_extractor: nn.Module, features_dim: int, activation_fn: Type[nn.Module] = nn.ReLU, normalize_images: bool = True, device: Union[th.device, str] = 'auto'): super(Critic, self).__init__(observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images, device=device) action_dim = get_action_dim(self.action_space) q1_net = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn) self.q1_net = nn.Sequential(*q1_net) q2_net = create_mlp(features_dim + action_dim, 1, net_arch, activation_fn) self.q2_net = nn.Sequential(*q2_net) def forward(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, th.Tensor]: # Learn the features extractor using the policy loss only with th.no_grad(): features = self.extract_features(obs) qvalue_input = th.cat([features, actions], dim=1) return self.q1_net(qvalue_input), self.q2_net(qvalue_input) def q1_forward(self, obs: th.Tensor, actions: th.Tensor) -> th.Tensor: with th.no_grad(): features = self.extract_features(obs) return self.q1_net(th.cat([features, actions], dim=1)) class TD3Policy(BasePolicy): """ Policy class (with both actor and critic) for TD3. :param observation_space: (gym.spaces.Space) Observation space :param action_space: (gym.spaces.Space) Action space :param lr_schedule: (Callable) Learning rate schedule (could be constant) :param net_arch: (Optional[List[int]]) The specification of the policy and value networks. :param device: (Union[th.device, str]) Device on which the code should run. :param activation_fn: (Type[nn.Module]) Activation function :param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use. :param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments to pass to the feature extractor. :param normalize_images: (bool) Whether to normalize images or not, dividing by 255.0 (True by default) :param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use, ``th.optim.Adam`` by default :param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments, excluding the learning rate, to pass to the optimizer """ def __init__(self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Callable, net_arch: Optional[List[int]] = None, device: Union[th.device, str] = 'auto', activation_fn: Type[nn.Module] = nn.ReLU, 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): super(TD3Policy, self).__init__(observation_space, action_space, device, features_extractor_class, features_extractor_kwargs, optimizer_class=optimizer_class, optimizer_kwargs=optimizer_kwargs, squash_output=True) # Default network architecture, from the original paper if net_arch is None: if features_extractor_class == FlattenExtractor: net_arch = [400, 300] else: net_arch = [] self.features_extractor = features_extractor_class(self.observation_space, **self.features_extractor_kwargs) self.features_dim = self.features_extractor.features_dim self.net_arch = net_arch self.activation_fn = activation_fn self.net_args = { 'observation_space': self.observation_space, 'action_space': self.action_space, 'features_extractor': self.features_extractor, 'features_dim': self.features_dim, 'net_arch': self.net_arch, 'activation_fn': self.activation_fn, 'normalize_images': normalize_images, 'device': device } self.actor, self.actor_target = None, None self.critic, self.critic_target = None, None self._build(lr_schedule) def _build(self, lr_schedule: Callable) -> None: self.actor = self.make_actor() self.actor_target = self.make_actor() self.actor_target.load_state_dict(self.actor.state_dict()) self.actor.optimizer = self.optimizer_class(self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs) self.critic = self.make_critic() self.critic_target = self.make_critic() self.critic_target.load_state_dict(self.critic.state_dict()) self.critic.optimizer = self.optimizer_class(self.critic.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs) def _get_data(self) -> Dict[str, Any]: data = super()._get_data() data.update(dict( net_arch=self.net_args['net_arch'], activation_fn=self.net_args['activation_fn'], lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone 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 make_actor(self) -> Actor: return Actor(**self.net_args).to(self.device) def make_critic(self) -> Critic: return Critic(**self.net_args).to(self.device) def forward(self, observation: th.Tensor, deterministic: bool = False): return self._predict(observation, deterministic=deterministic) def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor: return self.actor(observation, deterministic=deterministic) MlpPolicy = TD3Policy class CnnPolicy(TD3Policy): """ Policy class (with both actor and critic) for TD3. :param observation_space: (gym.spaces.Space) Observation space :param action_space: (gym.spaces.Space) Action space :param lr_schedule: (Callable) Learning rate schedule (could be constant) :param net_arch: (Optional[List[int]]) The specification of the policy and value networks. :param device: (Union[th.device, str]) Device on which the code should run. :param activation_fn: (Type[nn.Module]) Activation function :param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use. :param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments to pass to the feature extractor. :param normalize_images: (bool) Whether to normalize images or not, dividing by 255.0 (True by default) :param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use, ``th.optim.Adam`` by default :param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments, excluding the learning rate, to pass to the optimizer """ def __init__(self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Callable, net_arch: Optional[List[int]] = None, device: Union[th.device, str] = 'auto', activation_fn: Type[nn.Module] = nn.ReLU, features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN, features_extractor_kwargs: Optional[Dict[str, Any]] = None, normalize_images: bool = True, optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam, optimizer_kwargs: Optional[Dict[str, Any]] = None): super(CnnPolicy, self).__init__(observation_space, action_space, lr_schedule, net_arch, device, activation_fn, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs) register_policy("MlpPolicy", MlpPolicy) register_policy("CnnPolicy", CnnPolicy)