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 StateDependentNoiseDistribution
from stable_baselines3.common.policies import (
BaseModel,
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
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)
: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,
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,
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=not use_sde,
)
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
self.net_arch = net_arch
self.features_dim = features_dim
self.activation_fn = activation_fn
self.clip_noise = clip_noise
self.lr_sde = lr_sde
self.log_std_init = log_std_init
self.sde_net_arch = sde_net_arch
self.use_expln = use_expln
self.full_std = full_std
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.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_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,
clip_noise=self.clip_noise,
lr_sde=self.lr_sde,
full_std=self.full_std,
sde_net_arch=self.sde_net_arch,
use_expln=self.use_expln,
features_extractor=self.features_extractor,
)
)
return data
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 actions: (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 ValueFunction(BaseModel):
"""
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:
with th.no_grad():
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: (Union[th.device, str]) 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 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,
clip_noise: Optional[float] = None,
lr_sde: float = 3e-4,
sde_net_arch: Optional[List[int]] = None,
use_expln: bool = False,
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(TD3Policy, self).__init__(
observation_space,
action_space,
device,
features_extractor_class,
features_extractor_kwargs,
optimizer_class=optimizer_class,
optimizer_kwargs=optimizer_kwargs,
squash_output=True,
)
# Default network architecture, from the original paper
if net_arch is None:
if features_extractor_class == FlattenExtractor:
net_arch = [400, 300]
else:
net_arch = []
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,
"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.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
# 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 = 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())
self.critic.optimizer = self.optimizer_class(self.critic.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
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 _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"],
n_critics=self.critic_kwargs["n_critics"],
use_sde=self.actor_kwargs["use_sde"],
log_std_init=self.actor_kwargs["log_std_init"],
clip_noise=self.actor_kwargs["clip_noise"],
lr_sde=self.actor_kwargs["lr_sde"],
sde_net_arch=self.actor_kwargs["sde_net_arch"],
use_expln=self.actor_kwargs["use_expln"],
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) -> None:
return self.actor.reset_noise()
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, 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
class CnnPolicy(TD3Policy):
"""
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: (Union[th.device, str]) 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 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,
clip_noise: Optional[float] = None,
lr_sde: float = 3e-4,
sde_net_arch: Optional[List[int]] = None,
use_expln: bool = False,
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,
clip_noise,
lr_sde,
sde_net_arch,
use_expln,
features_extractor_class,
features_extractor_kwargs,
normalize_images,
optimizer_class,
optimizer_kwargs,
n_critics,
)
register_policy("MlpPolicy", MlpPolicy)
register_policy("CnnPolicy", CnnPolicy)