from typing import Any, Dict, List, Optional, Type, Union
import torch as th
from gymnasium import spaces
from torch import nn
from stable_baselines3.common.policies import BasePolicy, ContinuousCritic
from stable_baselines3.common.preprocessing import get_action_dim
from stable_baselines3.common.torch_layers import (
BaseFeaturesExtractor,
CombinedExtractor,
FlattenExtractor,
NatureCNN,
create_mlp,
get_actor_critic_arch,
)
from stable_baselines3.common.type_aliases import PyTorchObs, Schedule
class Actor(BasePolicy):
"""
Actor network (policy) for TD3.
:param observation_space: Obervation space
:param action_space: Action space
:param net_arch: Network architecture
:param features_extractor: Network to extract features
(a CNN when using images, a nn.Flatten() layer otherwise)
:param features_dim: Number of features
: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: spaces.Space,
action_space: spaces.Box,
net_arch: List[int],
features_extractor: nn.Module,
features_dim: int,
activation_fn: Type[nn.Module] = nn.ReLU,
normalize_images: bool = True,
):
super().__init__(
observation_space,
action_space,
features_extractor=features_extractor,
normalize_images=normalize_images,
squash_output=True,
)
self.net_arch = net_arch
self.features_dim = features_dim
self.activation_fn = activation_fn
action_dim = get_action_dim(self.action_space)
actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True)
# Deterministic action
self.mu = nn.Sequential(*actor_net)
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
def forward(self, obs: th.Tensor) -> th.Tensor:
# assert deterministic, 'The TD3 actor only outputs deterministic actions'
features = self.extract_features(obs, self.features_extractor)
return self.mu(features)
def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
# Note: the deterministic deterministic parameter is ignored in the case of TD3.
# Predictions are always deterministic.
return self(observation)
[docs]class TD3Policy(BasePolicy):
"""
Policy class (with both actor and critic) for TD3.
: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
:param n_critics: Number of critic networks to create.
:param share_features_extractor: Whether to share or not the features extractor
between the actor and the critic (this saves computation time)
"""
actor: Actor
actor_target: Actor
critic: ContinuousCritic
critic_target: ContinuousCritic
def __init__(
self,
observation_space: spaces.Space,
action_space: spaces.Box,
lr_schedule: Schedule,
net_arch: Optional[Union[List[int], Dict[str, 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,
n_critics: int = 2,
share_features_extractor: bool = False,
):
super().__init__(
observation_space,
action_space,
features_extractor_class,
features_extractor_kwargs,
optimizer_class=optimizer_class,
optimizer_kwargs=optimizer_kwargs,
squash_output=True,
normalize_images=normalize_images,
)
# Default network architecture, from the original paper
if net_arch is None:
if features_extractor_class == NatureCNN:
net_arch = [256, 256]
else:
net_arch = [400, 300]
actor_arch, critic_arch = get_actor_critic_arch(net_arch)
self.net_arch = net_arch
self.activation_fn = activation_fn
self.net_args = {
"observation_space": self.observation_space,
"action_space": self.action_space,
"net_arch": actor_arch,
"activation_fn": self.activation_fn,
"normalize_images": normalize_images,
}
self.actor_kwargs = self.net_args.copy()
self.critic_kwargs = self.net_args.copy()
self.critic_kwargs.update(
{
"n_critics": n_critics,
"net_arch": critic_arch,
"share_features_extractor": share_features_extractor,
}
)
self.share_features_extractor = share_features_extractor
self._build(lr_schedule)
def _build(self, lr_schedule: Schedule) -> None:
# Create actor and target
# the features extractor should not be shared
self.actor = self.make_actor(features_extractor=None)
self.actor_target = self.make_actor(features_extractor=None)
# Initialize the target to have the same weights as the actor
self.actor_target.load_state_dict(self.actor.state_dict())
self.actor.optimizer = self.optimizer_class(
self.actor.parameters(),
lr=lr_schedule(1), # type: ignore[call-arg]
**self.optimizer_kwargs,
)
if self.share_features_extractor:
self.critic = self.make_critic(features_extractor=self.actor.features_extractor)
# Critic target should not share the features extractor with critic
# but it can share it with the actor target as actor and critic are sharing
# the same features_extractor too
# NOTE: as a result the effective poliak (soft-copy) coefficient for the features extractor
# will be 2 * tau instead of tau (updated one time with the actor, a second time with the critic)
self.critic_target = self.make_critic(features_extractor=self.actor_target.features_extractor)
else:
# Create new features extractor for each network
self.critic = self.make_critic(features_extractor=None)
self.critic_target = self.make_critic(features_extractor=None)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic.optimizer = self.optimizer_class(
self.critic.parameters(),
lr=lr_schedule(1), # type: ignore[call-arg]
**self.optimizer_kwargs,
)
# Target networks should always be in eval mode
self.actor_target.set_training_mode(False)
self.critic_target.set_training_mode(False)
def _get_constructor_parameters(self) -> Dict[str, Any]:
data = super()._get_constructor_parameters()
data.update(
dict(
net_arch=self.net_arch,
activation_fn=self.net_args["activation_fn"],
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,
share_features_extractor=self.share_features_extractor,
)
)
return data
def make_actor(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> Actor:
actor_kwargs = self._update_features_extractor(self.actor_kwargs, features_extractor)
return Actor(**actor_kwargs).to(self.device)
def make_critic(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> ContinuousCritic:
critic_kwargs = self._update_features_extractor(self.critic_kwargs, features_extractor)
return ContinuousCritic(**critic_kwargs).to(self.device)
[docs] def forward(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
return self._predict(observation, deterministic=deterministic)
def _predict(self, observation: PyTorchObs, deterministic: bool = False) -> th.Tensor:
# Note: the deterministic deterministic parameter is ignored in the case of TD3.
# Predictions are always deterministic.
return self.actor(observation)
[docs] def set_training_mode(self, mode: bool) -> None:
"""
Put the policy in either training or evaluation mode.
This affects certain modules, such as batch normalisation and dropout.
:param mode: if true, set to training mode, else set to evaluation mode
"""
self.actor.set_training_mode(mode)
self.critic.set_training_mode(mode)
self.training = mode
MlpPolicy = TD3Policy
[docs]class CnnPolicy(TD3Policy):
"""
Policy class (with both actor and critic) for TD3.
: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
:param n_critics: Number of critic networks to create.
:param share_features_extractor: Whether to share or not the features extractor
between the actor and the critic (this saves computation time)
"""
def __init__(
self,
observation_space: spaces.Space,
action_space: spaces.Box,
lr_schedule: Schedule,
net_arch: Optional[Union[List[int], Dict[str, 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,
n_critics: int = 2,
share_features_extractor: bool = False,
):
super().__init__(
observation_space,
action_space,
lr_schedule,
net_arch,
activation_fn,
features_extractor_class,
features_extractor_kwargs,
normalize_images,
optimizer_class,
optimizer_kwargs,
n_critics,
share_features_extractor,
)