mirror of
https://github.com/saymrwulf/stable-baselines3.git
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249 lines
11 KiB
Python
249 lines
11 KiB
Python
from typing import Optional, List, Tuple, Callable, Union
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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 torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp, BaseNetwork, \
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create_sde_feature_extractor
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from torchy_baselines.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
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# CAP the standard deviation of the actor
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LOG_STD_MAX = 2
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LOG_STD_MIN = -20
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class Actor(BaseNetwork):
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"""
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Actor network (policy) for SAC.
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:param obs_dim: (int) Dimension of the observation
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:param action_dim: (int) Dimension of the action space
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:param net_arch: ([int]) Network architecture
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:param activation_fn: (nn.Module) Activation function
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:param use_sde: (bool) Whether to use State Dependent Exploration or not
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:param log_std_init: (float) Initial value for the log standard deviation
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:param full_std: (bool) Whether to use (n_features x n_actions) parameters
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for the std instead of only (n_features,) when using SDE.
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:param sde_net_arch: ([int]) Network architecture for extracting features
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when using SDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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:param use_expln: (bool) Use `expln()` function instead of `exp()` when using SDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, `exp()` is usually enough.
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:param clip_mean: (float) Clip the mean output when using SDE to avoid numerical instability.
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"""
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def __init__(self, obs_dim: int,
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action_dim: int,
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net_arch: List[int],
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activation_fn: nn.Module = nn.ReLU,
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use_sde: bool = False,
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log_std_init: float = -3,
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full_std: bool = True,
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sde_net_arch: Optional[List[int]] = None,
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use_expln: bool = False,
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clip_mean: float = 2.0):
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super(Actor, self).__init__()
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latent_pi_net = create_mlp(obs_dim, -1, net_arch, activation_fn)
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self.latent_pi = nn.Sequential(*latent_pi_net)
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self.use_sde = use_sde
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self.sde_feature_extractor = None
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if self.use_sde:
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latent_sde_dim = net_arch[-1]
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# Separate feature extractor for SDE
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if sde_net_arch is not None:
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self.sde_feature_extractor, latent_sde_dim = create_sde_feature_extractor(obs_dim, sde_net_arch,
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activation_fn)
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self.action_dist = StateDependentNoiseDistribution(action_dim, full_std=full_std, use_expln=use_expln,
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learn_features=True, squash_output=True)
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self.mu, self.log_std = self.action_dist.proba_distribution_net(latent_dim=net_arch[-1],
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latent_sde_dim=latent_sde_dim,
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log_std_init=log_std_init)
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# Avoid numerical issues by limiting the mean of the Gaussian
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# to be in [-clip_mean, clip_mean]
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if clip_mean > 0.0:
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self.mu = nn.Sequential(self.mu, nn.Hardtanh(min_val=-clip_mean, max_val=clip_mean))
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else:
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self.action_dist = SquashedDiagGaussianDistribution(action_dim)
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self.mu = nn.Linear(net_arch[-1], action_dim)
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self.log_std = nn.Linear(net_arch[-1], action_dim)
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def get_std(self) -> th.Tensor:
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"""
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Retrieve the standard deviation of the action distribution.
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Only useful when using SDE.
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It corresponds to `th.exp(log_std)` in the normal case,
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but is slightly different when using `expln` function
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(cf StateDependentNoiseDistribution doc).
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:return: (th.Tensor)
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"""
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assert isinstance(self.action_dist, StateDependentNoiseDistribution), 'get_std() is only available when using SDE'
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return self.action_dist.get_std(self.log_std)
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def reset_noise(self, batch_size: int = 1) -> None:
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"""
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Sample new weights for the exploration matrix, when using SDE.
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:param batch_size: (int)
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"""
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assert isinstance(self.action_dist, StateDependentNoiseDistribution), 'reset_noise() is only available when using SDE'
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self.action_dist.sample_weights(self.log_std, batch_size=batch_size)
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def _get_latent(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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latent_pi = self.latent_pi(obs)
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if self.sde_feature_extractor is not None:
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latent_sde = self.sde_feature_extractor(obs)
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else:
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latent_sde = latent_pi
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return latent_pi, latent_sde
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def get_action_dist_params(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
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latent_pi, latent_sde = self._get_latent(obs)
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if self.use_sde:
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mean_actions, log_std = self.mu(latent_pi), self.log_std
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else:
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mean_actions, log_std = self.mu(latent_pi), self.log_std(latent_pi)
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# Original Implementation to cap the standard deviation
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log_std = th.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)
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return mean_actions, log_std, latent_sde
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def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
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mean_actions, log_std, latent_sde = self.get_action_dist_params(obs)
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if self.use_sde:
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# Note: the action is squashed
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action, _ = self.action_dist.proba_distribution(mean_actions, log_std, latent_sde,
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deterministic=deterministic)
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else:
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# Note: the action is squashed
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action, _ = self.action_dist.proba_distribution(mean_actions, log_std,
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deterministic=deterministic)
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return action
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def action_log_prob(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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mean_actions, log_std, latent_sde = self.get_action_dist_params(obs)
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if self.use_sde:
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action, log_prob = self.action_dist.log_prob_from_params(mean_actions, self.log_std, latent_sde)
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else:
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action, log_prob = self.action_dist.log_prob_from_params(mean_actions, log_std)
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return action, log_prob
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class Critic(BaseNetwork):
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"""
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Critic network (q-value function) for SAC.
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:param obs_dim: (int) Dimension of the observation
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:param action_dim: (int) Dimension of the action space
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:param net_arch: ([int]) Network architecture
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:param activation_fn: (nn.Module) Activation function
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"""
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def __init__(self, obs_dim: int,
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action_dim: int,
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net_arch: List[int],
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activation_fn: nn.Module = nn.ReLU):
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super(Critic, self).__init__()
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q1_net = create_mlp(obs_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(obs_dim + action_dim, 1, net_arch, activation_fn)
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self.q2_net = nn.Sequential(*q2_net)
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self.q_networks = [self.q1_net, self.q2_net]
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def forward(self, obs: th.Tensor, action: th.Tensor) -> List[th.Tensor]:
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qvalue_input = th.cat([obs, action], dim=1)
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return [q_net(qvalue_input) for q_net in self.q_networks]
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class SACPolicy(BasePolicy):
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"""
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Policy class (with both actor and critic) for SAC.
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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 learning_rate: (callable) Learning rate schedule (could be constant)
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:param net_arch: ([int or dict]) The specification of the policy and value networks.
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:param device: (str or th.device) Device on which the code should run.
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:param activation_fn: (nn.Module) Activation function
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:param use_sde: (bool) Whether to use State Dependent Exploration or not
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:param log_std_init: (float) Initial value for the log standard deviation
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:param sde_net_arch: ([int]) Network architecture for extracting features
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when using SDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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:param use_expln: (bool) Use `expln()` function instead of `exp()` when using SDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, `exp()` is usually enough.
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:param clip_mean: (float) Clip the mean output when using SDE to avoid numerical instability.
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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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learning_rate: Callable,
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net_arch: Optional[List[int]] = None,
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device: Union[th.device, str] = 'cpu',
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activation_fn: nn.Module = nn.ReLU,
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use_sde: bool = False,
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log_std_init: float = -3,
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sde_net_arch: Optional[List[int]] = None,
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use_expln: bool = False,
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clip_mean: float = 2.0):
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super(SACPolicy, self).__init__(observation_space, action_space, device, squash_output=True)
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if net_arch is None:
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net_arch = [256, 256]
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self.obs_dim = self.observation_space.shape[0]
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self.action_dim = self.action_space.shape[0]
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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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'obs_dim': self.obs_dim,
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'action_dim': self.action_dim,
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'net_arch': self.net_arch,
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'activation_fn': self.activation_fn
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}
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self.actor_kwargs = self.net_args.copy()
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sde_kwargs = {
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'use_sde': use_sde,
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'log_std_init': log_std_init,
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'sde_net_arch': sde_net_arch,
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'use_expln': use_expln,
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'clip_mean': clip_mean
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}
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self.actor_kwargs.update(sde_kwargs)
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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(learning_rate)
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def _build(self, learning_rate: Callable) -> None:
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self.actor = self.make_actor()
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self.actor.optimizer = th.optim.Adam(self.actor.parameters(), lr=learning_rate(1))
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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 = th.optim.Adam(self.critic.parameters(), lr=learning_rate(1))
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def make_actor(self) -> Actor:
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return Actor(**self.actor_kwargs).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, obs: th.Tensor) -> th.Tensor:
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return self.actor(obs)
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def predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self.actor.forward(observation, deterministic)
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MlpPolicy = SACPolicy
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register_policy("MlpPolicy", MlpPolicy)
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