481 lines
20 KiB
Python
481 lines
20 KiB
Python
from typing import Any, Callable, Dict, List, Optional, Tuple, Type
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import gym
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import torch as th
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from stable_baselines3.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
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from stable_baselines3.common.policies import BaseModel, BasePolicy, create_sde_features_extractor, register_policy
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from stable_baselines3.common.preprocessing import get_action_dim
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from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
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from torch import nn as nn
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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(BasePolicy):
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"""
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Actor network (policy) for TQC.
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:param observation_space: (gym.spaces.Space) Obervation space
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:param action_space: (gym.spaces.Space) Action space
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:param net_arch: ([int]) Network architecture
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:param features_extractor: (nn.Module) Network to extract features
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(a CNN when using images, a nn.Flatten() layer otherwise)
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:param features_dim: (int) Number of features
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:param activation_fn: (Type[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 gSDE.
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:param sde_net_arch: ([int]) Network architecture for extracting features
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when using gSDE. 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 gSDE 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 gSDE to avoid numerical instability.
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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net_arch: List[int],
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features_extractor: nn.Module,
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features_dim: int,
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activation_fn: Type[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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normalize_images: bool = True,
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):
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super(Actor, self).__init__(
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observation_space,
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action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images,
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squash_output=True,
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)
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# Save arguments to re-create object at loading
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self.use_sde = use_sde
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self.sde_features_extractor = None
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self.sde_net_arch = sde_net_arch
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self.net_arch = net_arch
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self.features_dim = features_dim
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self.activation_fn = activation_fn
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self.log_std_init = log_std_init
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self.sde_net_arch = sde_net_arch
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self.use_expln = use_expln
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self.full_std = full_std
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self.clip_mean = clip_mean
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action_dim = get_action_dim(self.action_space)
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latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn)
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self.latent_pi = nn.Sequential(*latent_pi_net)
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last_layer_dim = net_arch[-1] if len(net_arch) > 0 else features_dim
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if self.use_sde:
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latent_sde_dim = last_layer_dim
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# Separate feature extractor for gSDE
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if sde_net_arch is not None:
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self.sde_features_extractor, latent_sde_dim = create_sde_features_extractor(
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features_dim, sde_net_arch, activation_fn
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)
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self.action_dist = StateDependentNoiseDistribution(
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action_dim, full_std=full_std, use_expln=use_expln, learn_features=True, squash_output=True
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)
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self.mu, self.log_std = self.action_dist.proba_distribution_net(
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latent_dim=last_layer_dim, latent_sde_dim=latent_sde_dim, log_std_init=log_std_init
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)
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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(last_layer_dim, action_dim)
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self.log_std = nn.Linear(last_layer_dim, action_dim)
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def _get_data(self) -> Dict[str, Any]:
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data = super()._get_data()
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data.update(
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dict(
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net_arch=self.net_arch,
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features_dim=self.features_dim,
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activation_fn=self.activation_fn,
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use_sde=self.use_sde,
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log_std_init=self.log_std_init,
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full_std=self.full_std,
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sde_net_arch=self.sde_net_arch,
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use_expln=self.use_expln,
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features_extractor=self.features_extractor,
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clip_mean=self.clip_mean,
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)
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)
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return data
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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 gSDE.
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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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msg = "get_std() is only available when using gSDE"
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assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
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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 gSDE.
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:param batch_size: (int)
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"""
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msg = "reset_noise() is only available when using gSDE"
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assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
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self.action_dist.sample_weights(self.log_std, batch_size=batch_size)
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def get_action_dist_params(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]:
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"""
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Get the parameters for the action distribution.
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:param obs: (th.Tensor)
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:return: (Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]])
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Mean, standard deviation and optional keyword arguments.
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"""
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features = self.extract_features(obs)
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latent_pi = self.latent_pi(features)
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mean_actions = self.mu(latent_pi)
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if self.use_sde:
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latent_sde = latent_pi
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if self.sde_features_extractor is not None:
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latent_sde = self.sde_features_extractor(features)
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return mean_actions, self.log_std, dict(latent_sde=latent_sde)
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# Unstructured exploration (Original implementation)
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log_std = 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, {}
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def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
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mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
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# Note: the action is squashed
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return self.action_dist.actions_from_params(mean_actions, log_std, deterministic=deterministic, **kwargs)
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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, kwargs = self.get_action_dist_params(obs)
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# return action and associated log prob
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return self.action_dist.log_prob_from_params(mean_actions, log_std, **kwargs)
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self.forward(observation, deterministic)
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def evaluate_actions(self, obs: th.Tensor, actions: th.Tensor) -> th.Tensor:
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"""
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Evaluate actions according to the current policy,
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given the observations. Only useful when using SDE.
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:param obs: (th.Tensor)
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:param actions: (th.Tensor)
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:return: (th.Tensor) log likelihood of taking those actions
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"""
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mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
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self.action_dist.proba_distribution(mean_actions, log_std, **kwargs)
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return self.action_dist.log_prob(actions)
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class Critic(BaseModel):
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"""
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Critic network (q-value function) for TQC.
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:param observation_space: (gym.spaces.Space) Obervation space
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:param action_space: (gym.spaces.Space) Action space
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:param net_arch: ([int]) Network architecture
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:param features_extractor: (nn.Module) Network to extract features
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(a CNN when using images, a nn.Flatten() layer otherwise)
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:param features_dim: (int) Number of features
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:param activation_fn: (Type[nn.Module]) Activation function
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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net_arch: List[int],
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features_extractor: nn.Module,
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features_dim: int,
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activation_fn: Type[nn.Module] = nn.ReLU,
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normalize_images: bool = True,
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n_quantiles: int = 25,
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n_critics: int = 2,
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):
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super().__init__(
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observation_space,
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action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images,
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)
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action_dim = get_action_dim(self.action_space)
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self.q_networks = []
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self.n_quantiles = n_quantiles
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self.n_critics = n_critics
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self.quantiles_total = n_quantiles * n_critics
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for i in range(n_critics):
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qf_net = create_mlp(features_dim + action_dim, n_quantiles, net_arch, activation_fn)
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qf_net = nn.Sequential(*qf_net)
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self.add_module(f"qf{i}", qf_net)
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self.q_networks.append(qf_net)
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def forward(self, obs: th.Tensor, action: th.Tensor) -> List[th.Tensor]:
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# Learn the features extractor using the policy loss only
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# this is much faster
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with th.no_grad():
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features = self.extract_features(obs)
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qvalue_input = th.cat([features, action], dim=1)
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quantiles = th.stack(tuple(qf(qvalue_input) for qf in self.q_networks), dim=1)
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return quantiles
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class TQCPolicy(BasePolicy):
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"""
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Policy class (with both actor and critic) for TQC.
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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 lr_schedule: (callable) Learning rate schedule (could be constant)
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:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
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:param activation_fn: (Type[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 gSDE. 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 gSDE 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 gSDE to avoid numerical instability.
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:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
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:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
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to pass to the feature extractor.
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
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excluding the learning rate, to pass to the optimizer
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"""
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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lr_schedule: Callable,
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net_arch: Optional[List[int]] = None,
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activation_fn: Type[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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features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
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features_extractor_kwargs: Optional[Dict[str, Any]] = None,
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normalize_images: bool = True,
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optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
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optimizer_kwargs: Optional[Dict[str, Any]] = None,
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n_quantiles: int = 25,
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n_critics: int = 2,
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):
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super(TQCPolicy, self).__init__(
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observation_space,
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action_space,
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features_extractor_class,
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features_extractor_kwargs,
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optimizer_class=optimizer_class,
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optimizer_kwargs=optimizer_kwargs,
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squash_output=True,
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)
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if net_arch is None:
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if features_extractor_class == FlattenExtractor:
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net_arch = [256, 256]
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else:
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net_arch = []
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# Create shared features extractor
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self.features_extractor = features_extractor_class(self.observation_space, **self.features_extractor_kwargs)
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self.features_dim = self.features_extractor.features_dim
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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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"observation_space": self.observation_space,
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"action_space": self.action_space,
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"features_extractor": self.features_extractor,
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"features_dim": self.features_dim,
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"net_arch": self.net_arch,
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"activation_fn": self.activation_fn,
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"normalize_images": normalize_images,
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}
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self.actor_kwargs = self.net_args.copy()
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self.critic_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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tqc_kwargs = {"n_quantiles": n_quantiles, "n_critics": n_critics}
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self.critic_kwargs.update(tqc_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(lr_schedule)
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def _build(self, lr_schedule: Callable) -> None:
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self.actor = self.make_actor()
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self.actor.optimizer = self.optimizer_class(self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
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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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# Do not optimize the shared feature extractor with the critic loss
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# otherwise, there are gradient computation issues
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# Another solution: having duplicated features extractor but requires more memory and computation
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critic_parameters = [param for name, param in self.critic.named_parameters() if "features_extractor" not in name]
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self.critic.optimizer = self.optimizer_class(critic_parameters, lr=lr_schedule(1), **self.optimizer_kwargs)
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def _get_data(self) -> Dict[str, Any]:
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data = super()._get_data()
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data.update(
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dict(
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net_arch=self.net_args["net_arch"],
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activation_fn=self.net_args["activation_fn"],
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use_sde=self.actor_kwargs["use_sde"],
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log_std_init=self.actor_kwargs["log_std_init"],
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sde_net_arch=self.actor_kwargs["sde_net_arch"],
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use_expln=self.actor_kwargs["use_expln"],
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clip_mean=self.actor_kwargs["clip_mean"],
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lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
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optimizer_class=self.optimizer_class,
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optimizer_kwargs=self.optimizer_kwargs,
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features_extractor_class=self.features_extractor_class,
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features_extractor_kwargs=self.features_extractor_kwargs,
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)
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)
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return data
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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 gSDE.
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:param batch_size: (int)
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"""
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self.actor.reset_noise(batch_size=batch_size)
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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.critic_kwargs).to(self.device)
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def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self._predict(obs, deterministic=deterministic)
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self.actor(observation, deterministic)
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MlpPolicy = TQCPolicy
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class CnnPolicy(TQCPolicy):
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"""
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Policy class (with both actor and critic) for TQC.
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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 lr_schedule: (callable) Learning rate schedule (could be constant)
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:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
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:param activation_fn: (Type[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 gSDE. 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 gSDE 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 gSDE to avoid numerical instability.
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:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
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:param normalize_images: (bool) Whether to normalize images or not,
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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,
|
|
activation_fn: Type[nn.Module] = nn.ReLU,
|
|
use_sde: bool = False,
|
|
log_std_init: float = -3,
|
|
sde_net_arch: Optional[List[int]] = None,
|
|
use_expln: bool = False,
|
|
clip_mean: float = 2.0,
|
|
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,
|
|
n_quantiles: int = 25,
|
|
n_critics: int = 2,
|
|
):
|
|
super(CnnPolicy, self).__init__(
|
|
observation_space,
|
|
action_space,
|
|
lr_schedule,
|
|
net_arch,
|
|
activation_fn,
|
|
use_sde,
|
|
log_std_init,
|
|
sde_net_arch,
|
|
use_expln,
|
|
clip_mean,
|
|
features_extractor_class,
|
|
features_extractor_kwargs,
|
|
normalize_images,
|
|
optimizer_class,
|
|
optimizer_kwargs,
|
|
n_quantiles,
|
|
n_critics,
|
|
)
|
|
|
|
|
|
register_policy("MlpPolicy", MlpPolicy)
|
|
register_policy("CnnPolicy", CnnPolicy)
|