412 lines
16 KiB
Python
412 lines
16 KiB
Python
from functools import partial
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from typing import Any, Dict, List, Optional, Tuple, Type, Union
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import gym
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import numpy as np
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import torch as th
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from stable_baselines3.common.policies import BasePolicy
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from stable_baselines3.common.torch_layers import (
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BaseFeaturesExtractor,
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CombinedExtractor,
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FlattenExtractor,
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MlpExtractor,
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NatureCNN,
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)
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from stable_baselines3.common.type_aliases import Schedule
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from torch import nn
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from sb3_contrib.common.maskable.distributions import MaskableDistribution, make_masked_proba_distribution
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class MaskableActorCriticPolicy(BasePolicy):
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"""
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Policy class for actor-critic algorithms (has both policy and value prediction).
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Used by A2C, PPO and the likes.
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:param observation_space: Observation space
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:param action_space: Action space
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:param lr_schedule: Learning rate schedule (could be constant)
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param ortho_init: Whether to use or not orthogonal initialization
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:param features_extractor_class: Features extractor to use.
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:param features_extractor_kwargs: Keyword arguments
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to pass to the features extractor.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: 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: Schedule,
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net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
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activation_fn: Type[nn.Module] = nn.Tanh,
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ortho_init: bool = True,
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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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):
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if optimizer_kwargs is None:
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optimizer_kwargs = {}
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# Small values to avoid NaN in Adam optimizer
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if optimizer_class == th.optim.Adam:
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optimizer_kwargs["eps"] = 1e-5
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super().__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=False,
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)
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# Default network architecture, from stable-baselines
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if net_arch is None:
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if features_extractor_class == NatureCNN:
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net_arch = []
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else:
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net_arch = [dict(pi=[64, 64], vf=[64, 64])]
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self.net_arch = net_arch
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self.activation_fn = activation_fn
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self.ortho_init = ortho_init
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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.normalize_images = normalize_images
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# Action distribution
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self.action_dist = make_masked_proba_distribution(action_space)
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self._build(lr_schedule)
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def forward(
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self,
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obs: th.Tensor,
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deterministic: bool = False,
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action_masks: Optional[np.ndarray] = None,
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) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
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"""
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Forward pass in all the networks (actor and critic)
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:param obs: Observation
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:param deterministic: Whether to sample or use deterministic actions
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:param action_masks: Action masks to apply to the action distribution
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:return: action, value and log probability of the action
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"""
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# Preprocess the observation if needed
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features = self.extract_features(obs)
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latent_pi, latent_vf = self.mlp_extractor(features)
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# Evaluate the values for the given observations
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values = self.value_net(latent_vf)
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distribution = self._get_action_dist_from_latent(latent_pi)
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if action_masks is not None:
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distribution.apply_masking(action_masks)
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actions = distribution.get_actions(deterministic=deterministic)
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log_prob = distribution.log_prob(actions)
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return actions, values, log_prob
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def _get_constructor_parameters(self) -> Dict[str, Any]:
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data = super()._get_constructor_parameters()
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data.update(
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dict(
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net_arch=self.net_arch,
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activation_fn=self.activation_fn,
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lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
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ortho_init=self.ortho_init,
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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 _build_mlp_extractor(self) -> None:
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"""
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Create the policy and value networks.
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Part of the layers can be shared.
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"""
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# Note: If net_arch is None and some features extractor is used,
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# net_arch here is an empty list and mlp_extractor does not
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# really contain any layers (acts like an identity module).
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self.mlp_extractor = MlpExtractor(
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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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device=self.device,
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)
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def _build(self, lr_schedule: Schedule) -> None:
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"""
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Create the networks and the optimizer.
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:param lr_schedule: Learning rate schedule
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lr_schedule(1) is the initial learning rate
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"""
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self._build_mlp_extractor()
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self.action_net = self.action_dist.proba_distribution_net(latent_dim=self.mlp_extractor.latent_dim_pi)
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self.value_net = nn.Linear(self.mlp_extractor.latent_dim_vf, 1)
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# Init weights: use orthogonal initialization
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# with small initial weight for the output
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if self.ortho_init:
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# TODO: check for features_extractor
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# Values from stable-baselines.
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# features_extractor/mlp values are
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# originally from openai/baselines (default gains/init_scales).
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module_gains = {
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self.features_extractor: np.sqrt(2),
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self.mlp_extractor: np.sqrt(2),
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self.action_net: 0.01,
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self.value_net: 1,
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}
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for module, gain in module_gains.items():
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module.apply(partial(self.init_weights, gain=gain))
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# Setup optimizer with initial learning rate
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self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
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def _get_action_dist_from_latent(self, latent_pi: th.Tensor) -> MaskableDistribution:
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"""
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Retrieve action distribution given the latent codes.
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:param latent_pi: Latent code for the actor
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:return: Action distribution
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"""
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action_logits = self.action_net(latent_pi)
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return self.action_dist.proba_distribution(action_logits=action_logits)
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def _predict(
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self,
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observation: th.Tensor,
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deterministic: bool = False,
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action_masks: Optional[np.ndarray] = None,
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) -> th.Tensor:
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"""
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Get the action according to the policy for a given observation.
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:param observation:
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:param deterministic: Whether to use stochastic or deterministic actions
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:param action_masks: Action masks to apply to the action distribution
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:return: Taken action according to the policy
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"""
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return self.get_distribution(observation, action_masks).get_actions(deterministic=deterministic)
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def predict(
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self,
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observation: Union[np.ndarray, Dict[str, np.ndarray]],
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state: Optional[Tuple[np.ndarray, ...]] = None,
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episode_start: Optional[np.ndarray] = None,
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deterministic: bool = False,
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action_masks: Optional[np.ndarray] = None,
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) -> Tuple[np.ndarray, Optional[Tuple[np.ndarray, ...]]]:
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"""
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Get the policy action and state from an observation (and optional state).
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Includes sugar-coating to handle different observations (e.g. normalizing images).
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:param observation: the input observation
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:param state: The last states (can be None, used in recurrent policies)
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:param mask: The last masks (can be None, used in recurrent policies)
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:param deterministic: Whether or not to return deterministic actions.
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:param action_masks: Action masks to apply to the action distribution
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:return: the model's action and the next state
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(used in recurrent policies)
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"""
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# TODO (GH/1): add support for RNN policies
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# if state is None:
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# state = self.initial_state
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# if mask is None:
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# mask = [False for _ in range(self.n_envs)]
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# Switch to eval mode (this affects batch norm / dropout)
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self.set_training_mode(False)
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observation, vectorized_env = self.obs_to_tensor(observation)
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with th.no_grad():
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actions = self._predict(observation, deterministic=deterministic, action_masks=action_masks)
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# Convert to numpy
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actions = actions.cpu().numpy()
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if isinstance(self.action_space, gym.spaces.Box):
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if self.squash_output:
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# Rescale to proper domain when using squashing
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actions = self.unscale_action(actions)
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else:
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# Actions could be on arbitrary scale, so clip the actions to avoid
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# out of bound error (e.g. if sampling from a Gaussian distribution)
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actions = np.clip(actions, self.action_space.low, self.action_space.high)
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if not vectorized_env:
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if state is not None:
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raise ValueError("Error: The environment must be vectorized when using recurrent policies.")
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actions = actions[0]
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return actions, state
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def evaluate_actions(
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self,
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obs: th.Tensor,
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actions: th.Tensor,
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action_masks: Optional[np.ndarray] = None,
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) -> Tuple[th.Tensor, 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.
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:param obs:
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:param actions:
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:return: estimated value, log likelihood of taking those actions
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and entropy of the action distribution.
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"""
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features = self.extract_features(obs)
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latent_pi, latent_vf = self.mlp_extractor(features)
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distribution = self._get_action_dist_from_latent(latent_pi)
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if action_masks is not None:
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distribution.apply_masking(action_masks)
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log_prob = distribution.log_prob(actions)
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values = self.value_net(latent_vf)
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return values, log_prob, distribution.entropy()
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def get_distribution(self, obs: th.Tensor, action_masks: Optional[np.ndarray] = None) -> MaskableDistribution:
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"""
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Get the current policy distribution given the observations.
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:param obs:
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:param action_masks:
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:return: the action distribution.
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"""
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features = self.extract_features(obs)
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latent_pi = self.mlp_extractor.forward_actor(features)
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distribution = self._get_action_dist_from_latent(latent_pi)
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if action_masks is not None:
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distribution.apply_masking(action_masks)
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return distribution
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def predict_values(self, obs: th.Tensor) -> th.Tensor:
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"""
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Get the estimated values according to the current policy given the observations.
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:param obs:
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:return: the estimated values.
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"""
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features = self.extract_features(obs)
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latent_vf = self.mlp_extractor.forward_critic(features)
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return self.value_net(latent_vf)
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class MaskableActorCriticCnnPolicy(MaskableActorCriticPolicy):
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"""
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CNN policy class for actor-critic algorithms (has both policy and value prediction).
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Used by A2C, PPO and the likes.
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:param observation_space: Observation space
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:param action_space: Action space
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:param lr_schedule: Learning rate schedule (could be constant)
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param ortho_init: Whether to use or not orthogonal initialization
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:param features_extractor_class: Features extractor to use.
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:param features_extractor_kwargs: Keyword arguments
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to pass to the features extractor.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: 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: Schedule,
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net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
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activation_fn: Type[nn.Module] = nn.Tanh,
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ortho_init: bool = True,
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features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
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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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):
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super().__init__(
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observation_space,
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action_space,
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lr_schedule,
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net_arch,
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activation_fn,
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ortho_init,
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features_extractor_class,
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features_extractor_kwargs,
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normalize_images,
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optimizer_class,
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optimizer_kwargs,
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)
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class MaskableMultiInputActorCriticPolicy(MaskableActorCriticPolicy):
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"""
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MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).
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Used by A2C, PPO and the likes.
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:param observation_space: Observation space (Tuple)
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:param action_space: Action space
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:param lr_schedule: Learning rate schedule (could be constant)
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:param net_arch: The specification of the policy and value networks.
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:param activation_fn: Activation function
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:param ortho_init: Whether to use or not orthogonal initialization
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:param features_extractor_class: Uses the CombinedExtractor
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:param features_extractor_kwargs: Keyword arguments
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to pass to the feature extractor.
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:param normalize_images: Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: 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.Dict,
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action_space: gym.spaces.Space,
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lr_schedule: Schedule,
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net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
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activation_fn: Type[nn.Module] = nn.Tanh,
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ortho_init: bool = True,
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features_extractor_class: Type[BaseFeaturesExtractor] = CombinedExtractor,
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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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):
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super().__init__(
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observation_space,
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action_space,
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lr_schedule,
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net_arch,
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activation_fn,
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ortho_init,
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features_extractor_class,
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features_extractor_kwargs,
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normalize_images,
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optimizer_class,
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optimizer_kwargs,
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)
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