mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-06-27 03:11:57 +00:00
365 lines
18 KiB
Python
365 lines
18 KiB
Python
from typing import Optional, List, Tuple, Callable, Union, Dict, Type, Any
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from functools import partial
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import gym
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import torch as th
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import torch.nn as nn
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import numpy as np
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from stable_baselines3.common.policies import (BasePolicy, register_policy, MlpExtractor,
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create_sde_features_extractor, NatureCNN,
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BaseFeaturesExtractor, FlattenExtractor)
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from stable_baselines3.common.distributions import (make_proba_distribution, Distribution,
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DiagGaussianDistribution, CategoricalDistribution,
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StateDependentNoiseDistribution)
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class PPOPolicy(BasePolicy):
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"""
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Policy class (with both actor and critic) for A2C and derivates (PPO).
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:param observation_space: (gym.spaces.Space) Observation space
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:param action_space: (gym.spaces.Space) Action space
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:param lr_schedule: (Callable) Learning rate schedule (could be constant)
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:param net_arch: ([int or dict]) The specification of the policy and value networks.
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:param device: (str or th.device) Device on which the code should run.
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:param activation_fn: (Type[nn.Module]) Activation function
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:param ortho_init: (bool) Whether to use or not orthogonal initialization
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:param use_sde: (bool) Whether to use State Dependent Exploration or not
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:param log_std_init: (float) Initial value for the log standard deviation
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:param full_std: (bool) Whether to use (n_features x n_actions) parameters
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for the std instead of only (n_features,) when using SDE
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:param sde_net_arch: ([int]) Network architecture for extracting features
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when using SDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` 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 squash_output: (bool) Whether to squash the output using a tanh function,
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this allows to ensure boundaries when using SDE.
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:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
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:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
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to pass to the feature extractor.
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: (Optional[Dict[str, Any]]) 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__(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: Callable,
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net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
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device: Union[th.device, str] = 'auto',
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activation_fn: Type[nn.Module] = nn.Tanh,
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ortho_init: bool = True,
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use_sde: bool = False,
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log_std_init: float = 0.0,
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full_std: bool = True,
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sde_net_arch: Optional[List[int]] = None,
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use_expln: bool = False,
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squash_output: bool = False,
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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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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(PPOPolicy, self).__init__(observation_space, action_space,
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device,
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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=squash_output)
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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 == FlattenExtractor:
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net_arch = [dict(pi=[64, 64], vf=[64, 64])]
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else:
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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.ortho_init = ortho_init
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self.features_extractor = features_extractor_class(self.observation_space,
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**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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self.log_std_init = log_std_init
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dist_kwargs = None
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# Keyword arguments for SDE distribution
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if use_sde:
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dist_kwargs = {
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'full_std': full_std,
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'squash_output': squash_output,
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'use_expln': use_expln,
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'learn_features': sde_net_arch is not None
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}
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self.sde_features_extractor = None
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self.sde_net_arch = sde_net_arch
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self.use_sde = use_sde
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self.dist_kwargs = dist_kwargs
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# Action distribution
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self.action_dist = make_proba_distribution(action_space, use_sde=use_sde, dist_kwargs=dist_kwargs)
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self._build(lr_schedule)
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def _get_data(self) -> Dict[str, Any]:
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data = super()._get_data()
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data.update(dict(
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net_arch=self.net_arch,
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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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squash_output=self.dist_kwargs['squash_output'] if self.dist_kwargs else None,
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full_std=self.dist_kwargs['full_std'] if self.dist_kwargs else None,
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sde_net_arch=self.dist_kwargs['sde_net_arch'] if self.dist_kwargs else None,
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use_expln=self.dist_kwargs['use_expln'] if self.dist_kwargs else None,
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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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return data
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def reset_noise(self, n_envs: int = 1) -> None:
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"""
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Sample new weights for the exploration matrix.
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:param n_envs: (int)
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"""
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assert isinstance(self.action_dist,
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StateDependentNoiseDistribution), 'reset_noise() is only available when using SDE'
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self.action_dist.sample_weights(self.log_std, batch_size=n_envs)
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def _build(self, lr_schedule: Callable) -> None:
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"""
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Create the networks and the optimizer.
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:param lr_schedule: (Callable) Learning rate schedule
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lr_schedule(1) is the initial learning rate
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"""
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self.mlp_extractor = MlpExtractor(self.features_dim, net_arch=self.net_arch,
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activation_fn=self.activation_fn, device=self.device)
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latent_dim_pi = self.mlp_extractor.latent_dim_pi
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# Separate feature extractor for SDE
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if self.sde_net_arch is not None:
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self.sde_features_extractor, latent_sde_dim = create_sde_features_extractor(self.features_dim,
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self.sde_net_arch,
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self.activation_fn)
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if isinstance(self.action_dist, DiagGaussianDistribution):
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self.action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=latent_dim_pi,
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log_std_init=self.log_std_init)
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elif isinstance(self.action_dist, StateDependentNoiseDistribution):
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latent_sde_dim = latent_dim_pi if self.sde_net_arch is None else latent_sde_dim
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self.action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=latent_dim_pi,
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latent_sde_dim=latent_sde_dim,
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log_std_init=self.log_std_init)
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elif isinstance(self.action_dist, CategoricalDistribution):
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self.action_net = self.action_dist.proba_distribution_net(latent_dim=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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for module in [self.features_extractor, self.mlp_extractor,
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self.action_net, self.value_net]:
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# Values from stable-baselines, TODO: check why
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gain = {
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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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}[module]
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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 forward(self, obs: th.Tensor,
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deterministic: bool = False) -> 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: (th.Tensor) Observation
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:param deterministic: (bool) Whether to sample or use deterministic actions
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:return: (Tuple[th.Tensor, th.Tensor, th.Tensor]) action, value and log probability of the action
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"""
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latent_pi, latent_vf, latent_sde = self._get_latent(obs)
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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, latent_sde=latent_sde)
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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_latent(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor, th.Tensor]:
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"""
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Get the latent code (i.e., activations of the last layer of each network)
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for the different networks.
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:param obs: (th.Tensor) Observation
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:return: (Tuple[th.Tensor, th.Tensor, th.Tensor]) Latent codes
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for the actor, the value function and for SDE function
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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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# Features for sde
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latent_sde = latent_pi
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if self.sde_features_extractor is not None:
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latent_sde = self.sde_features_extractor(features)
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return latent_pi, latent_vf, latent_sde
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def _get_action_dist_from_latent(self, latent_pi: th.Tensor,
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latent_sde: Optional[th.Tensor] = None) -> Distribution:
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"""
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Retrieve action distribution given the latent codes.
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:param latent_pi: (th.Tensor) Latent code for the actor
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:param latent_sde: (Optional[th.Tensor]) Latent code for the SDE exploration function
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:return: (Distribution) Action distribution
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"""
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mean_actions = self.action_net(latent_pi)
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if isinstance(self.action_dist, DiagGaussianDistribution):
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return self.action_dist.proba_distribution(mean_actions, self.log_std)
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elif isinstance(self.action_dist, CategoricalDistribution):
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# Here mean_actions are the logits before the softmax
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return self.action_dist.proba_distribution(action_logits=mean_actions)
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elif isinstance(self.action_dist, StateDependentNoiseDistribution):
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return self.action_dist.proba_distribution(mean_actions, self.log_std, latent_sde)
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else:
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raise ValueError('Invalid action distribution')
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> 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: (th.Tensor)
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:param deterministic: (bool) Whether to use stochastic or deterministic actions
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:return: (th.Tensor) Taken action according to the policy
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"""
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latent_pi, _, latent_sde = self._get_latent(observation)
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distribution = self._get_action_dist_from_latent(latent_pi, latent_sde)
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return distribution.get_actions(deterministic=deterministic)
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def evaluate_actions(self, obs: th.Tensor,
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actions: th.Tensor) -> 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: (th.Tensor)
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:param actions: (th.Tensor)
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:return: (th.Tensor, th.Tensor, th.Tensor) 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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latent_pi, latent_vf, latent_sde = self._get_latent(obs)
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distribution = self._get_action_dist_from_latent(latent_pi, latent_sde)
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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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MlpPolicy = PPOPolicy
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class CnnPolicy(PPOPolicy):
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"""
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CnnPolicy class (with both actor and critic) for A2C and derivates (PPO).
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:param observation_space: (gym.spaces.Space) Observation space
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:param action_space: (gym.spaces.Space) Action space
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:param lr_schedule: (Callable) Learning rate schedule (could be constant)
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:param net_arch: ([int or dict]) The specification of the policy and value networks.
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:param device: (str or th.device) Device on which the code should run.
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:param activation_fn: (Type[nn.Module]) Activation function
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:param ortho_init: (bool) Whether to use or not orthogonal initialization
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:param use_sde: (bool) Whether to use State Dependent Exploration or not
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:param log_std_init: (float) Initial value for the log standard deviation
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:param full_std: (bool) Whether to use (n_features x n_actions) parameters
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for the std instead of only (n_features,) when using SDE
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:param sde_net_arch: ([int]) Network architecture for extracting features
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when using SDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` 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 squash_output: (bool) Whether to squash the output using a tanh function,
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this allows to ensure boundaries when using SDE.
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:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
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:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
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to pass to the feature extractor.
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
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``th.optim.Adam`` by default
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:param optimizer_kwargs: (Optional[Dict[str, Any]]) 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__(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: Callable,
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net_arch: Optional[List[Union[int, Dict[str, List[int]]]]] = None,
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device: Union[th.device, str] = 'auto',
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activation_fn: Type[nn.Module] = nn.Tanh,
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ortho_init: bool = True,
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use_sde: bool = False,
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log_std_init: float = 0.0,
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full_std: bool = True,
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sde_net_arch: Optional[List[int]] = None,
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use_expln: bool = False,
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squash_output: bool = False,
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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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super(CnnPolicy, self).__init__(observation_space,
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action_space,
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lr_schedule,
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net_arch,
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device,
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activation_fn,
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ortho_init,
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use_sde,
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log_std_init,
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full_std,
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sde_net_arch,
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use_expln,
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squash_output,
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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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register_policy("MlpPolicy", MlpPolicy)
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register_policy("CnnPolicy", CnnPolicy)
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