from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
import gym
import torch as th
from torch import nn as nn
from stable_baselines3.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
from stable_baselines3.common.policies import BasePolicy, ContinuousCritic, create_sde_features_extractor, register_policy
from stable_baselines3.common.preprocessing import get_action_dim
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
# CAP the standard deviation of the actor
LOG_STD_MAX = 2
LOG_STD_MIN = -20
class Actor(BasePolicy):
"""
Actor network (policy) for SAC.
: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 full_std: (bool) Whether to use (n_features x n_actions) parameters
for the std instead of only (n_features,) when using gSDE.
:param sde_net_arch: ([int]) Network architecture for extracting features
when using gSDE. 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 gSDE 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 clip_mean: (float) Clip the mean output when using gSDE to avoid numerical instability.
:param normalize_images: (bool) Whether to normalize images or not,
dividing by 255.0 (True by default)
:param device: (Union[th.device, str]) Device on which the code should run.
"""
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,
full_std: bool = True,
sde_net_arch: Optional[List[int]] = None,
use_expln: bool = False,
clip_mean: float = 2.0,
normalize_images: bool = True,
device: Union[th.device, str] = "auto",
):
super(Actor, self).__init__(
observation_space,
action_space,
features_extractor=features_extractor,
normalize_images=normalize_images,
device=device,
squash_output=True,
)
# Save arguments to re-create object at loading
self.use_sde = use_sde
self.sde_features_extractor = None
self.sde_net_arch = sde_net_arch
self.net_arch = net_arch
self.features_dim = features_dim
self.activation_fn = activation_fn
self.log_std_init = log_std_init
self.sde_net_arch = sde_net_arch
self.use_expln = use_expln
self.full_std = full_std
self.clip_mean = clip_mean
action_dim = get_action_dim(self.action_space)
latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn)
self.latent_pi = nn.Sequential(*latent_pi_net)
last_layer_dim = net_arch[-1] if len(net_arch) > 0 else features_dim
if self.use_sde:
latent_sde_dim = last_layer_dim
# Separate feature extractor for gSDE
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
)
self.action_dist = StateDependentNoiseDistribution(
action_dim, full_std=full_std, use_expln=use_expln, learn_features=True, squash_output=True
)
self.mu, self.log_std = self.action_dist.proba_distribution_net(
latent_dim=last_layer_dim, latent_sde_dim=latent_sde_dim, log_std_init=log_std_init
)
# Avoid numerical issues by limiting the mean of the Gaussian
# to be in [-clip_mean, clip_mean]
if clip_mean > 0.0:
self.mu = nn.Sequential(self.mu, nn.Hardtanh(min_val=-clip_mean, max_val=clip_mean))
else:
self.action_dist = SquashedDiagGaussianDistribution(action_dim)
self.mu = nn.Linear(last_layer_dim, action_dim)
self.log_std = nn.Linear(last_layer_dim, action_dim)
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,
use_sde=self.use_sde,
log_std_init=self.log_std_init,
full_std=self.full_std,
sde_net_arch=self.sde_net_arch,
use_expln=self.use_expln,
features_extractor=self.features_extractor,
clip_mean=self.clip_mean,
)
)
return data
def get_std(self) -> th.Tensor:
"""
Retrieve the standard deviation of the action distribution.
Only useful when using gSDE.
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)
"""
msg = "get_std() is only available when using gSDE"
assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
return self.action_dist.get_std(self.log_std)
def reset_noise(self, batch_size: int = 1) -> None:
"""
Sample new weights for the exploration matrix, when using gSDE.
:param batch_size: (int)
"""
msg = "reset_noise() is only available when using gSDE"
assert isinstance(self.action_dist, StateDependentNoiseDistribution), msg
self.action_dist.sample_weights(self.log_std, batch_size=batch_size)
def get_action_dist_params(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]]:
"""
Get the parameters for the action distribution.
:param obs: (th.Tensor)
:return: (Tuple[th.Tensor, th.Tensor, Dict[str, th.Tensor]])
Mean, standard deviation and optional keyword arguments.
"""
features = self.extract_features(obs)
latent_pi = self.latent_pi(features)
mean_actions = self.mu(latent_pi)
if self.use_sde:
latent_sde = latent_pi
if self.sde_features_extractor is not None:
latent_sde = self.sde_features_extractor(features)
return mean_actions, self.log_std, dict(latent_sde=latent_sde)
# Unstructured exploration (Original implementation)
log_std = self.log_std(latent_pi)
# Original Implementation to cap the standard deviation
log_std = th.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)
return mean_actions, log_std, {}
def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
# Note: the action is squashed
return self.action_dist.actions_from_params(mean_actions, log_std, deterministic=deterministic, **kwargs)
def action_log_prob(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
mean_actions, log_std, kwargs = self.get_action_dist_params(obs)
# return action and associated log prob
return self.action_dist.log_prob_from_params(mean_actions, log_std, **kwargs)
def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
return self.forward(observation, deterministic)
class SACPolicy(BasePolicy):
"""
Policy class (with both actor and critic) for SAC.
: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 gSDE. 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 gSDE 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 clip_mean: (float) Clip the mean output when using gSDE to avoid numerical instability.
: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
:param n_critics: (int) Number of critic networks to create.
"""
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,
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] = 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,
n_critics: int = 2,
):
super(SACPolicy, self).__init__(
observation_space,
action_space,
device,
features_extractor_class,
features_extractor_kwargs,
optimizer_class=optimizer_class,
optimizer_kwargs=optimizer_kwargs,
squash_output=True,
)
if net_arch is None:
if features_extractor_class == FlattenExtractor:
net_arch = [256, 256]
else:
net_arch = []
# Create shared features extractor
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.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.actor_kwargs = self.net_args.copy()
sde_kwargs = {
"use_sde": use_sde,
"log_std_init": log_std_init,
"sde_net_arch": sde_net_arch,
"use_expln": use_expln,
"clip_mean": clip_mean,
}
self.actor_kwargs.update(sde_kwargs)
self.critic_kwargs = self.net_args.copy()
self.critic_kwargs.update({"n_critics": n_critics})
self.actor, self.actor_target = None, None
self.critic, self.critic_target = None, None
self._build(lr_schedule)
def _build(self, lr_schedule: Callable) -> None:
self.actor = self.make_actor()
self.actor.optimizer = self.optimizer_class(self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
self.critic = self.make_critic()
self.critic_target = self.make_critic()
self.critic_target.load_state_dict(self.critic.state_dict())
# Do not optimize the shared feature extractor with the critic loss
# otherwise, there are gradient computation issues
# Another solution: having duplicated features extractor but requires more memory and computation
critic_parameters = [param for name, param in self.critic.named_parameters() if "features_extractor" not in name]
self.critic.optimizer = self.optimizer_class(critic_parameters, lr=lr_schedule(1), **self.optimizer_kwargs)
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"],
use_sde=self.actor_kwargs["use_sde"],
log_std_init=self.actor_kwargs["log_std_init"],
sde_net_arch=self.actor_kwargs["sde_net_arch"],
use_expln=self.actor_kwargs["use_expln"],
clip_mean=self.actor_kwargs["clip_mean"],
n_critics=self.critic_kwargs["n_critics"],
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
def reset_noise(self, batch_size: int = 1) -> None:
"""
Sample new weights for the exploration matrix, when using gSDE.
:param batch_size: (int)
"""
self.actor.reset_noise(batch_size=batch_size)
def make_actor(self) -> Actor:
return Actor(**self.actor_kwargs).to(self.device)
def make_critic(self) -> ContinuousCritic:
return ContinuousCritic(**self.critic_kwargs).to(self.device)
def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
return self._predict(obs, deterministic=deterministic)
def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
return self.actor(observation, deterministic)
MlpPolicy = SACPolicy
class CnnPolicy(SACPolicy):
"""
Policy class (with both actor and critic) for SAC.
: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 gSDE. 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 gSDE 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 clip_mean: (float) Clip the mean output when using gSDE to avoid numerical instability.
: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
:param n_critics: (int) Number of critic networks to create.
"""
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,
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_critics: int = 2,
):
super(CnnPolicy, self).__init__(
observation_space,
action_space,
lr_schedule,
net_arch,
device,
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_critics,
)
register_policy("MlpPolicy", MlpPolicy)
register_policy("CnnPolicy", CnnPolicy)