from typing import Optional, List, Tuple, Callable, Union, Type import gym import torch as th import torch.nn as nn from torchy_baselines.common.preprocessing import get_action_dim, get_obs_dim from torchy_baselines.common.policies import (BasePolicy, register_policy, create_mlp, create_sde_features_extractor) from torchy_baselines.common.distributions import StateDependentNoiseDistribution, Distribution 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 use_sde: (bool) Whether to use State Dependent Exploration or not :param log_std_init: (float) Initial value for the log standard deviation :param clip_noise: (float) Clip the magnitude of the noise :param lr_sde: (float) Learning rate for the standard deviation of the noise :param full_std: (bool) Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using SDE. :param sde_net_arch: ([int]) Network architecture for extracting features when using SDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using SDE to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough. :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, net_arch: List[int], features_extractor: nn.Module, features_dim: int, activation_fn: Type[nn.Module] = nn.ReLU, use_sde: bool = False, log_std_init: float = -3, clip_noise: Optional[float] = None, lr_sde: float = 3e-4, full_std: bool = False, sde_net_arch: Optional[List[int]] = None, use_expln: bool = False, normalize_images: bool = True): super(Actor, self).__init__(observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images) self.latent_pi, self.log_std = None, None self.weights_dist, self.exploration_mat = None, None self.use_sde, self.sde_optimizer = use_sde, None self.full_std = full_std self.sde_features_extractor = None self.features_extractor = features_extractor self.normalize_images = normalize_images action_dim = get_action_dim(self.action_space) if use_sde: latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn, squash_output=False) self.latent_pi = nn.Sequential(*latent_pi_net) latent_sde_dim = net_arch[-1] learn_features = sde_net_arch is not None # Separate feature extractor for SDE if sde_net_arch is not None: self.sde_features_extractor, latent_sde_dim = create_sde_features_extractor(features_dim, sde_net_arch, activation_fn) # Create state dependent noise matrix (SDE) self.action_dist = StateDependentNoiseDistribution(action_dim, full_std=full_std, use_expln=use_expln, squash_output=False, learn_features=learn_features) action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=net_arch[-1], latent_sde_dim=latent_sde_dim, log_std_init=log_std_init) # Squash output self.mu = nn.Sequential(action_net, nn.Tanh()) self.clip_noise = clip_noise self.sde_optimizer = th.optim.Adam([self.log_std], lr=lr_sde) self.reset_noise() else: actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True) self.mu = nn.Sequential(*actor_net) def get_std(self) -> th.Tensor: """ Retrieve the standard deviation of the action distribution. Only useful when using SDE. It corresponds to ``th.exp(log_std)`` in the normal case, but is slightly different when using ``expln`` function (cf StateDependentNoiseDistribution doc). :return: (th.Tensor) """ return self.action_dist.get_std(self.log_std) def _get_latent(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]: features = self.extract_features(obs) latent_pi = self.latent_pi(features) latent_sde = self.sde_features_extractor(features) if self.sde_features_extractor is not None else latent_pi return latent_pi, latent_sde def evaluate_actions(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, th.Tensor]: """ Evaluate actions according to the current policy, given the observations. Only useful when using SDE. :param obs: (th.Tensor) :param action: (th.Tensor) :return: (th.Tensor, th.Tensor) log likelihood of taking those actions and entropy of the action distribution. """ latent_pi, latent_sde = self._get_latent(obs) mean_actions = self.mu(latent_pi) distribution = self.action_dist.proba_distribution(mean_actions, self.log_std, latent_sde) log_prob = distribution.log_prob(actions) return log_prob, distribution.entropy() def reset_noise(self) -> None: """ Sample new weights for the exploration matrix, when using SDE. """ self.action_dist.sample_weights(self.log_std) def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor: if self.use_sde: latent_pi, latent_sde = self._get_latent(obs) if deterministic: return self.mu(latent_pi) noise = self.action_dist.get_noise(latent_sde) if self.clip_noise is not None: noise = th.clamp(noise, -self.clip_noise, self.clip_noise) # TODO: Replace with squashing -> need to account for that in the sde update # -> set squash_output=True in the action_dist? # NOTE: the clipping is done in the rollout for now return self.mu(latent_pi) + noise else: 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) """ 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): super(Critic, self).__init__(observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images) 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]: 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: features = self.extract_features(obs) return self.q1_net(th.cat([features, actions], dim=1)) class ValueFunction(BasePolicy): """ Value function for TD3 when doing on-policy exploration with SDE. :param observation_space: (gym.spaces.Space) Obervation space :param action_space: (gym.spaces.Space) Action space :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 net_arch: (Optional[List[int]]) Network architecture :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, activation_fn: Type[nn.Module] = nn.Tanh, normalize_images: bool = True): super(ValueFunction, self).__init__(observation_space, action_space, features_extractor=features_extractor, normalize_images=normalize_images) if net_arch is None: net_arch = [64, 64] vf_net = create_mlp(features_dim, 1, net_arch, activation_fn) self.vf_net = nn.Sequential(*vf_net) def forward(self, obs: th.Tensor) -> th.Tensor: features = self.extract_features(obs) return self.vf_net(features) 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: (str or th.device) Device on which the code should run. :param activation_fn: (Type[nn.Module]) Activation function :param use_sde: (bool) Whether to use State Dependent Exploration or not :param log_std_init: (float) Initial value for the log standard deviation :param sde_net_arch: ([int]) Network architecture for extracting features when using SDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features. :param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using SDE to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough. :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, lr_schedule: Callable, net_arch: Optional[List[int]] = None, device: Union[th.device, str] = 'cpu', activation_fn: Type[nn.Module] = nn.ReLU, use_sde: bool = False, log_std_init: float = -3, clip_noise: Optional[float] = None, lr_sde: float = 3e-4, sde_net_arch: Optional[List[int]] = None, use_expln: bool = False, normalize_images: bool = True): super(TD3Policy, self).__init__(observation_space, action_space, device, squash_output=True) # Default network architecture, from the original paper if net_arch is None: net_arch = [400, 300] # In the future, features_extractor will be replaced with a CNN self.features_extractor = nn.Flatten() self.features_dim = get_obs_dim(self.observation_space) 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 } self.actor_kwargs = self.net_args.copy() sde_kwargs = { 'use_sde': use_sde, 'log_std_init': log_std_init, 'clip_noise': clip_noise, 'lr_sde': lr_sde, 'sde_net_arch': sde_net_arch, 'use_expln': use_expln } self.actor_kwargs.update(sde_kwargs) self.actor, self.actor_target = None, None self.critic, self.critic_target = None, None # For SDE only self.use_sde = use_sde self.vf_net = None self.log_std_init = log_std_init 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 = th.optim.Adam(self.actor.parameters(), lr=lr_schedule(1)) self.critic = self.make_critic() self.critic_target = self.make_critic() self.critic_target.load_state_dict(self.critic.state_dict()) self.critic.optimizer = th.optim.Adam(self.critic.parameters(), lr=lr_schedule(1)) if self.use_sde: self.vf_net = ValueFunction(self.observation_space, self.action_space, features_extractor=self.features_extractor, features_dim=self.features_dim) self.actor.sde_optimizer.add_param_group({'params': self.vf_net.parameters()}) # pytype: disable=attribute-error def reset_noise(self) -> None: return self.actor.reset_noise() def make_actor(self) -> Actor: return Actor(**self.actor_kwargs).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 register_policy("MlpPolicy", MlpPolicy)