515 lines
20 KiB
Python
515 lines
20 KiB
Python
from typing import Any, Dict, List, Optional, Tuple, Type, Union
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import torch as th
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from gymnasium import spaces
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from stable_baselines3.common.policies import BaseModel, 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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NatureCNN,
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create_mlp,
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get_actor_critic_arch,
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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 torch.distributions import Categorical
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class Actor(BasePolicy):
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"""
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Actor network (policy) for SACD
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:param observation_space: Obervation space
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:param action_space: Action space
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:param net_arch: Network architecture
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:param features_extractor: Network to extract features
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(a CNN when using images, a nn.Flatten() layer otherwise)
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:param features_dim: Number of features
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:param activation_fn: Activation function
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:param use_sde: Whether to use State Dependent Exploration or not
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:param log_std_init: Initial value for the log standard deviation
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:param full_std: Whether to use (n_features x n_actions) parameters
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for the std instead of only (n_features,) when using gSDE.
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:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
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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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"""
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action_space: spaces.Discrete
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def __init__(
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self,
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observation_space: spaces.Space,
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action_space: spaces.Box,
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net_arch: List[int],
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features_extractor: nn.Module,
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features_dim: int,
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activation_fn: Type[nn.Module] = nn.Softmax,
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use_sde: bool = False,
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log_std_init: float = -3,
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full_std: bool = True,
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use_expln: bool = False,
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clip_mean: float = 2.0,
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normalize_images: bool = True,
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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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features_extractor=features_extractor,
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normalize_images=normalize_images,
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# squash_output=True,
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squash_output=False,
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)
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# Save arguments to re-create object at loading
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self.use_sde = use_sde
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self.sde_features_extractor = None
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self.net_arch = net_arch
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self.features_dim = features_dim
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self.activation_fn = activation_fn
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self.log_std_init = log_std_init
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self.use_expln = use_expln
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self.full_std = full_std
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self.clip_mean = clip_mean
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num_actions = self.action_space.n
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latent_pi_net = create_mlp(features_dim, num_actions, net_arch, activation_fn)
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self.latent_pi = nn.Sequential(*latent_pi_net)
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self.output_activation = nn.Softmax(dim=1)
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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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features_dim=self.features_dim,
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activation_fn=self.activation_fn,
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use_sde=self.use_sde,
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log_std_init=self.log_std_init,
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full_std=self.full_std,
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use_expln=self.use_expln,
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features_extractor=self.features_extractor,
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clip_mean=self.clip_mean,
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)
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)
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return data
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def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
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features = self.extract_features(obs, self.features_extractor)
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action_probabilities = self.output_activation(self.latent_pi(features))
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if deterministic:
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action = th.argmax(action_probabilities)
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else:
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# random action according to policy
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dist = Categorical(probs=action_probabilities)
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action = dist.sample()
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return action
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def action_log_prob(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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features = self.extract_features(obs, self.features_extractor)
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action_prob = self.output_activation(self.latent_pi(features))
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# Have to deal with situation of 0.0 probabilities because we can't do log 0
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z = action_prob == 0.0
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z = z.float() * 1e-8
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log_action_prob = th.log(action_prob + z)
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return action_prob, log_action_prob
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self(observation, deterministic)
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class DiscreteCritic(BaseModel):
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"""
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Critic network(s) for DDPG/SAC/TD3.
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It represents the action-state value function (Q-value function).
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Compared to A2C/PPO critics, this one represents the Q-value
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and takes the continuous action as input. It is concatenated with the state
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and then fed to the network which outputs a single value: Q(s, a).
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For more recent algorithms like SAC/TD3, multiple networks
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are created to give different estimates.
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By default, it creates two critic networks used to reduce overestimation
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thanks to clipped Q-learning (cf TD3 paper).
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:param observation_space: Obervation space
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:param action_space: Action space
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:param net_arch: Network architecture
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:param features_extractor: Network to extract features
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(a CNN when using images, a nn.Flatten() layer otherwise)
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:param features_dim: Number of features
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:param activation_fn: Activation function
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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 n_critics: Number of critic networks to create.
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:param share_features_extractor: Whether the features extractor is shared or not
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between the actor and the critic (this saves computation time)
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"""
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action_space: spaces.Discrete
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def __init__(
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self,
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observation_space: spaces.Space,
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action_space: spaces.Discrete,
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net_arch: List[int],
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features_extractor: BaseFeaturesExtractor,
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features_dim: int,
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activation_fn: Type[nn.Module] = nn.ReLU,
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normalize_images: bool = True,
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n_critics: int = 2,
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share_features_extractor: bool = True,
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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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features_extractor=features_extractor,
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normalize_images=normalize_images,
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)
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num_actions = self.action_space.n
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self.share_features_extractor = share_features_extractor
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self.n_critics = n_critics
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self.q_networks = []
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for idx in range(n_critics):
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q_net = create_mlp(features_dim, num_actions, net_arch, activation_fn)
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q_net = nn.Sequential(*q_net)
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self.add_module(f"qf{idx}", q_net)
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self.q_networks.append(q_net)
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def get_crit_params(self, n):
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return self.q_networks[n].parameters()
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def forward(self, obs: th.Tensor) -> Tuple[th.Tensor, ...]:
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# Learn the features extractor using the policy loss only
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# when the features_extractor is shared with the actor
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with th.set_grad_enabled(not self.share_features_extractor):
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features = self.extract_features(obs, self.features_extractor)
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return tuple(q_net(features) for q_net in self.q_networks)
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class SACDPolicy(BasePolicy):
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"""
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Policy class (with both actor and critic) for SACD.
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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 use_sde: Whether to use State Dependent Exploration or not
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:param log_std_init: Initial value for the log standard deviation
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:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
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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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:param n_critics: Number of critic networks to create.
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:param share_features_extractor: Whether to share or not the features extractor
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between the actor and the critic (this saves computation time)
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"""
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actor: Actor
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critic: DiscreteCritic
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critic_target: DiscreteCritic
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def __init__(
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self,
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observation_space: spaces.Space,
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action_space: spaces.Box,
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lr_schedule: Schedule,
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net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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use_sde: bool = False,
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log_std_init: float = -3,
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use_expln: bool = False,
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clip_mean: float = 2.0,
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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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n_critics: int = 2,
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share_features_extractor: bool = False,
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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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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=True,
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normalize_images=normalize_images,
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)
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if net_arch is None:
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net_arch = [256, 256]
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actor_arch, critic_arch = get_actor_critic_arch(net_arch)
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self.net_arch = net_arch
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self.activation_fn = activation_fn
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self.net_args = {
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"observation_space": self.observation_space,
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"action_space": self.action_space,
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"net_arch": actor_arch,
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"activation_fn": self.activation_fn,
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"normalize_images": normalize_images,
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}
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self.actor_kwargs = self.net_args.copy()
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sde_kwargs = {
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"use_sde": use_sde,
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"log_std_init": log_std_init,
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"use_expln": use_expln,
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"clip_mean": clip_mean,
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}
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self.actor_kwargs.update(sde_kwargs)
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self.critic_kwargs = self.net_args.copy()
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self.critic_kwargs.update(
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{
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"n_critics": n_critics,
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"net_arch": critic_arch,
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"share_features_extractor": share_features_extractor,
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}
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)
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self.share_features_extractor = share_features_extractor
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self._build(lr_schedule)
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def _build(self, lr_schedule: Schedule) -> None:
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self.actor = self.make_actor()
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self.actor.optimizer = self.optimizer_class(
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self.actor.parameters(),
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lr=lr_schedule(1), # type: ignore[call-arg]
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**self.optimizer_kwargs,
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)
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if self.share_features_extractor:
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self.critic = self.make_critic(features_extractor=self.actor.features_extractor)
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# Do not optimize the shared features extractor with the critic loss
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# otherwise, there are gradient computation issues
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critic_parameters = [param for name, param in self.critic.named_parameters() if "features_extractor" not in name]
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else:
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# Create a separate features extractor for the critic
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# this requires more memory and computation
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self.critic = self.make_critic(features_extractor=None)
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critic_parameters = list(self.critic.parameters())
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# Critic target should not share the features extractor with critic
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self.critic_target = self.make_critic(features_extractor=None)
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self.critic_target.load_state_dict(self.critic.state_dict())
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self.critic.optimizer = self.optimizer_class(
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critic_parameters,
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lr=lr_schedule(1), # type: ignore[call-arg]
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**self.optimizer_kwargs,
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)
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# Target networks should always be in eval mode
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self.critic_target.set_training_mode(False)
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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.net_args["activation_fn"],
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use_sde=self.actor_kwargs["use_sde"],
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log_std_init=self.actor_kwargs["log_std_init"],
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use_expln=self.actor_kwargs["use_expln"],
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clip_mean=self.actor_kwargs["clip_mean"],
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n_critics=self.critic_kwargs["n_critics"],
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lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
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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 make_actor(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> Actor:
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actor_kwargs = self._update_features_extractor(self.actor_kwargs, features_extractor)
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return Actor(**actor_kwargs).to(self.device)
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def make_critic(self, features_extractor: Optional[BaseFeaturesExtractor] = None) -> DiscreteCritic:
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critic_kwargs = self._update_features_extractor(self.critic_kwargs, features_extractor)
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return DiscreteCritic(**critic_kwargs).to(self.device)
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def forward(self, obs: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self._predict(obs, deterministic=deterministic)
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self.actor(observation, deterministic)
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def set_training_mode(self, mode: bool) -> None:
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"""
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Put the policy in either training or evaluation mode.
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This affects certain modules, such as batch normalisation and dropout.
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:param mode: if true, set to training mode, else set to evaluation mode
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"""
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self.actor.set_training_mode(mode)
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self.critic.set_training_mode(mode)
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self.training = mode
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MlpPolicy = SACDPolicy
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class CnnPolicy(SACDPolicy):
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"""
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Policy class (with both actor and critic) for SACD.
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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 use_sde: Whether to use State Dependent Exploration or not
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:param log_std_init: Initial value for the log standard deviation
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:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
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:param features_extractor_class: Features extractor to use.
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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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:param n_critics: Number of critic networks to create.
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:param share_features_extractor: Whether to share or not the features extractor
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between the actor and the critic (this saves computation time)
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"""
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def __init__(
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self,
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observation_space: spaces.Space,
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action_space: spaces.Box,
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lr_schedule: Schedule,
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net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
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activation_fn: Type[nn.Module] = nn.ReLU,
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use_sde: bool = False,
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log_std_init: float = -3,
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use_expln: bool = False,
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clip_mean: float = 2.0,
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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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n_critics: int = 2,
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share_features_extractor: bool = False,
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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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use_sde,
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log_std_init,
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use_expln,
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clip_mean,
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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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n_critics,
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share_features_extractor,
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)
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class MultiInputPolicy(SACDPolicy):
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"""
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Policy class (with both actor and critic) for SACD.
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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 use_sde: Whether to use State Dependent Exploration or not
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:param log_std_init: Initial value for the log standard deviation
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:param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
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:param features_extractor_class: Features extractor to use.
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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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:param n_critics: Number of critic networks to create.
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:param share_features_extractor: Whether to share or not the features extractor
|
|
between the actor and the critic (this saves computation time)
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|
"""
|
|
|
|
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,
|
|
use_sde: bool = False,
|
|
log_std_init: float = -3,
|
|
use_expln: bool = False,
|
|
clip_mean: float = 2.0,
|
|
features_extractor_class: Type[BaseFeaturesExtractor] = CombinedExtractor,
|
|
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,
|
|
use_sde,
|
|
log_std_init,
|
|
use_expln,
|
|
clip_mean,
|
|
features_extractor_class,
|
|
features_extractor_kwargs,
|
|
normalize_images,
|
|
optimizer_class,
|
|
optimizer_kwargs,
|
|
n_critics,
|
|
share_features_extractor,
|
|
)
|