from typing import Optional, List, Callable, Union, Type, Any, Dict import gym import torch as th import torch.nn as nn from stable_baselines3.common.policies import BasePolicy, register_policy from stable_baselines3.common.torch_layers import create_mlp, NatureCNN, BaseFeaturesExtractor, FlattenExtractor class QNetwork(BasePolicy): """ Action-Value (Q-Value) network for DQN :param observation_space: (gym.spaces.Space) Observation space :param action_space: (gym.spaces.Space) Action space :param net_arch: (Optional[List[int]]) The specification of the policy and value networks. :param device: (str or th.device) Device on which the code should run. :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) """ def __init__(self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, features_extractor: nn.Module, features_dim: int, net_arch: Optional[List[int]] = None, device: Union[th.device, str] = 'auto', activation_fn: Type[nn.Module] = nn.ReLU, normalize_images: bool = True): super(QNetwork, self).__init__(observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images, device=device) if net_arch is None: net_arch = [64, 64] self.net_arch = net_arch self.activation_fn = activation_fn self.features_extractor = features_extractor self.features_dim = features_dim self.normalize_images = normalize_images action_dim = self.action_space.n # number of actions q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn) self.q_net = nn.Sequential(*q_net) def forward(self, obs: th.Tensor) -> th.Tensor: """ Predict the q-values. :param obs: (th.Tensor) Observation :return: (th.Tensor) The estimated Q-Value for each action. """ return self.q_net(self.extract_features(obs)) def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor: q_values = self.forward(observation) # Greedy action action = q_values.argmax(dim=1).reshape(-1) return action 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, epsilon=self.epsilon, )) return data class DQNPolicy(BasePolicy): """ Policy class with Q-Value Net and target net for DQN :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: (str or th.device) 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(DQNPolicy, self).__init__(observation_space, action_space, device, features_extractor_class, features_extractor_kwargs, optimizer_class=optimizer_class, optimizer_kwargs=optimizer_kwargs) if net_arch is None: if features_extractor_class == FlattenExtractor: net_arch = [64, 64] 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.normalize_images = normalize_images 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.q_net, self.q_net_target = None, None self._build(lr_schedule) def _build(self, lr_schedule: Callable) -> None: """ Create the network and the optimizer. :param lr_schedule: (Callable) Learning rate schedule lr_schedule(1) is the initial learning rate """ self.q_net = self.make_q_net() self.q_net_target = self.make_q_net() self.q_net_target.load_state_dict(self.q_net.state_dict()) # Setup optimizer with initial learning rate self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs) def make_q_net(self) -> QNetwork: return QNetwork(**self.net_args).to(self.device) def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor: return self._predict(obs, deterministic=deterministic) def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor: return self.q_net._predict(obs, deterministic=deterministic) 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 MlpPolicy = DQNPolicy class CnnPolicy(DQNPolicy): """ Policy class for DQN when using images as input. :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: (str or th.device) 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 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)