from typing import Any, Dict, List, Optional, Type import gym import torch as th from torch import nn from stable_baselines3.common.policies import BasePolicy, register_policy from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp from stable_baselines3.common.type_aliases import Schedule class QNetwork(BasePolicy): """ Action-Value (Q-Value) network for DQN :param observation_space: Observation space :param action_space: Action space :param net_arch: The specification of the policy and value networks. :param activation_fn: Activation function :param normalize_images: 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, 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, ) 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: Observation :return: 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_constructor_parameters(self) -> Dict[str, Any]: data = super()._get_constructor_parameters() 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 class DQNPolicy(BasePolicy): """ Policy class with Q-Value Net and target net for DQN :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 features_extractor_class: Features extractor to use. :param features_extractor_kwargs: Keyword arguments to pass to the features extractor. :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 """ def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[List[int]] = None, 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, 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.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, "net_arch": self.net_arch, "activation_fn": self.activation_fn, "normalize_images": normalize_images, } self.q_net, self.q_net_target = None, None self._build(lr_schedule) def _build(self, lr_schedule: Schedule) -> None: """ Create the network and the optimizer. :param lr_schedule: 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: # Make sure we always have separate networks for features extractors etc net_args = self._update_features_extractor(self.net_args, features_extractor=None) return QNetwork(**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_constructor_parameters(self) -> Dict[str, Any]: data = super()._get_constructor_parameters() 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: 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 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 """ def __init__( self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, lr_schedule: Schedule, net_arch: Optional[List[int]] = None, 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, activation_fn, features_extractor_class, features_extractor_kwargs, normalize_images, optimizer_class, optimizer_kwargs, ) register_policy("MlpPolicy", MlpPolicy) register_policy("CnnPolicy", CnnPolicy)