from functools import partial
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import gym
import numpy as np
import torch as th
from stable_baselines3.common.policies import BasePolicy
from stable_baselines3.common.torch_layers import (
BaseFeaturesExtractor,
CombinedExtractor,
FlattenExtractor,
MlpExtractor,
NatureCNN,
)
from stable_baselines3.common.type_aliases import Schedule
from torch import nn
from sb3_contrib.common.maskable.distributions import MaskableDistribution, make_masked_proba_distribution
[docs]class MaskableActorCriticPolicy(BasePolicy):
"""
Policy class for actor-critic algorithms (has both policy and value prediction).
Used by A2C, PPO and the likes.
: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 ortho_init: Whether to use or not orthogonal initialization
: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
"""
def __init__(
self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
lr_schedule: Schedule,
net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
activation_fn: Type[nn.Module] = nn.Tanh,
ortho_init: bool = True,
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,
):
if optimizer_kwargs is None:
optimizer_kwargs = {}
# Small values to avoid NaN in Adam optimizer
if optimizer_class == th.optim.Adam:
optimizer_kwargs["eps"] = 1e-5
super().__init__(
observation_space,
action_space,
features_extractor_class,
features_extractor_kwargs,
optimizer_class=optimizer_class,
optimizer_kwargs=optimizer_kwargs,
squash_output=False,
)
# Default network architecture, from stable-baselines
if net_arch is None:
if features_extractor_class == NatureCNN:
net_arch = []
else:
net_arch = [dict(pi=[64, 64], vf=[64, 64])]
self.net_arch = net_arch
self.activation_fn = activation_fn
self.ortho_init = ortho_init
self.features_extractor = features_extractor_class(self.observation_space, **self.features_extractor_kwargs)
self.features_dim = self.features_extractor.features_dim
self.normalize_images = normalize_images
# Action distribution
self.action_dist = make_masked_proba_distribution(action_space)
self._build(lr_schedule)
[docs] def forward(
self,
obs: th.Tensor,
deterministic: bool = False,
action_masks: Optional[np.ndarray] = None,
) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
"""
Forward pass in all the networks (actor and critic)
:param obs: Observation
:param deterministic: Whether to sample or use deterministic actions
:param action_masks: Action masks to apply to the action distribution
:return: action, value and log probability of the action
"""
# Preprocess the observation if needed
features = self.extract_features(obs)
latent_pi, latent_vf = self.mlp_extractor(features)
# Evaluate the values for the given observations
values = self.value_net(latent_vf)
distribution = self._get_action_dist_from_latent(latent_pi)
if action_masks is not None:
distribution.apply_masking(action_masks)
actions = distribution.get_actions(deterministic=deterministic)
log_prob = distribution.log_prob(actions)
return actions, values, log_prob
def _get_constructor_parameters(self) -> Dict[str, Any]:
data = super()._get_constructor_parameters()
data.update(
dict(
net_arch=self.net_arch,
activation_fn=self.activation_fn,
lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
ortho_init=self.ortho_init,
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 _build_mlp_extractor(self) -> None:
"""
Create the policy and value networks.
Part of the layers can be shared.
"""
# Note: If net_arch is None and some features extractor is used,
# net_arch here is an empty list and mlp_extractor does not
# really contain any layers (acts like an identity module).
self.mlp_extractor = MlpExtractor(
self.features_dim,
net_arch=self.net_arch,
activation_fn=self.activation_fn,
device=self.device,
)
def _build(self, lr_schedule: Schedule) -> None:
"""
Create the networks and the optimizer.
:param lr_schedule: Learning rate schedule
lr_schedule(1) is the initial learning rate
"""
self._build_mlp_extractor()
self.action_net = self.action_dist.proba_distribution_net(latent_dim=self.mlp_extractor.latent_dim_pi)
self.value_net = nn.Linear(self.mlp_extractor.latent_dim_vf, 1)
# Init weights: use orthogonal initialization
# with small initial weight for the output
if self.ortho_init:
# TODO: check for features_extractor
# Values from stable-baselines.
# features_extractor/mlp values are
# originally from openai/baselines (default gains/init_scales).
module_gains = {
self.features_extractor: np.sqrt(2),
self.mlp_extractor: np.sqrt(2),
self.action_net: 0.01,
self.value_net: 1,
}
for module, gain in module_gains.items():
module.apply(partial(self.init_weights, gain=gain))
# Setup optimizer with initial learning rate
self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
def _get_action_dist_from_latent(self, latent_pi: th.Tensor) -> MaskableDistribution:
"""
Retrieve action distribution given the latent codes.
:param latent_pi: Latent code for the actor
:return: Action distribution
"""
action_logits = self.action_net(latent_pi)
return self.action_dist.proba_distribution(action_logits=action_logits)
def _predict(
self,
observation: th.Tensor,
deterministic: bool = False,
action_masks: Optional[np.ndarray] = None,
) -> th.Tensor:
"""
Get the action according to the policy for a given observation.
:param observation:
:param deterministic: Whether to use stochastic or deterministic actions
:param action_masks: Action masks to apply to the action distribution
:return: Taken action according to the policy
"""
return self.get_distribution(observation, action_masks).get_actions(deterministic=deterministic)
[docs] def predict(
self,
observation: Union[np.ndarray, Dict[str, np.ndarray]],
state: Optional[Tuple[np.ndarray, ...]] = None,
episode_start: Optional[np.ndarray] = None,
deterministic: bool = False,
action_masks: Optional[np.ndarray] = None,
) -> Tuple[np.ndarray, Optional[Tuple[np.ndarray, ...]]]:
"""
Get the policy action from an observation (and optional hidden state).
Includes sugar-coating to handle different observations (e.g. normalizing images).
:param observation: the input observation
:param state: The last states (can be None, used in recurrent policies)
:param episode_start: The last masks (can be None, used in recurrent policies)
:param deterministic: Whether or not to return deterministic actions.
:param action_masks: Action masks to apply to the action distribution
:return: the model's action and the next state
(used in recurrent policies)
"""
# TODO (GH/1): add support for RNN policies
# if state is None:
# state = self.initial_state
# if episode_start is None:
# episode_start = [False for _ in range(self.n_envs)]
# Switch to eval mode (this affects batch norm / dropout)
self.set_training_mode(False)
observation, vectorized_env = self.obs_to_tensor(observation)
with th.no_grad():
actions = self._predict(observation, deterministic=deterministic, action_masks=action_masks)
# Convert to numpy
actions = actions.cpu().numpy()
if isinstance(self.action_space, gym.spaces.Box):
if self.squash_output:
# Rescale to proper domain when using squashing
actions = self.unscale_action(actions)
else:
# Actions could be on arbitrary scale, so clip the actions to avoid
# out of bound error (e.g. if sampling from a Gaussian distribution)
actions = np.clip(actions, self.action_space.low, self.action_space.high)
if not vectorized_env:
if state is not None:
raise ValueError("Error: The environment must be vectorized when using recurrent policies.")
actions = actions.squeeze(axis=0)
return actions, None
[docs] def evaluate_actions(
self,
obs: th.Tensor,
actions: th.Tensor,
action_masks: Optional[np.ndarray] = None,
) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
"""
Evaluate actions according to the current policy,
given the observations.
:param obs:
:param actions:
:return: estimated value, log likelihood of taking those actions
and entropy of the action distribution.
"""
features = self.extract_features(obs)
latent_pi, latent_vf = self.mlp_extractor(features)
distribution = self._get_action_dist_from_latent(latent_pi)
if action_masks is not None:
distribution.apply_masking(action_masks)
log_prob = distribution.log_prob(actions)
values = self.value_net(latent_vf)
return values, log_prob, distribution.entropy()
[docs] def get_distribution(self, obs: th.Tensor, action_masks: Optional[np.ndarray] = None) -> MaskableDistribution:
"""
Get the current policy distribution given the observations.
:param obs:
:param action_masks:
:return: the action distribution.
"""
features = self.extract_features(obs)
latent_pi = self.mlp_extractor.forward_actor(features)
distribution = self._get_action_dist_from_latent(latent_pi)
if action_masks is not None:
distribution.apply_masking(action_masks)
return distribution
[docs] def predict_values(self, obs: th.Tensor) -> th.Tensor:
"""
Get the estimated values according to the current policy given the observations.
:param obs:
:return: the estimated values.
"""
features = self.extract_features(obs)
latent_vf = self.mlp_extractor.forward_critic(features)
return self.value_net(latent_vf)
[docs]class MaskableActorCriticCnnPolicy(MaskableActorCriticPolicy):
"""
CNN policy class for actor-critic algorithms (has both policy and value prediction).
Used by A2C, PPO and the likes.
: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 ortho_init: Whether to use or not orthogonal initialization
: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
"""
def __init__(
self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
lr_schedule: Schedule,
net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
activation_fn: Type[nn.Module] = nn.Tanh,
ortho_init: bool = True,
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,
):
super().__init__(
observation_space,
action_space,
lr_schedule,
net_arch,
activation_fn,
ortho_init,
features_extractor_class,
features_extractor_kwargs,
normalize_images,
optimizer_class,
optimizer_kwargs,
)