mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-07-13 18:08:39 +00:00
194 lines
9.3 KiB
Python
194 lines
9.3 KiB
Python
from functools import partial
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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 torchy_baselines.common.policies import BasePolicy, register_policy, MlpExtractor, \
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create_sde_feature_extractor
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from torchy_baselines.common.distributions import make_proba_distribution,\
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DiagGaussianDistribution, CategoricalDistribution, 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 learning_rate: (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: (nn.Module) Activation function
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:param adam_epsilon: (float) Small values to avoid NaN in ADAM optimizer
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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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"""
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def __init__(self, observation_space, action_space,
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learning_rate, net_arch=None, device='cpu',
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activation_fn=nn.Tanh, adam_epsilon=1e-5,
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ortho_init=True, use_sde=False,
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log_std_init=0.0, full_std=True,
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sde_net_arch=None, use_expln=False, squash_output=False):
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super(PPOPolicy, self).__init__(observation_space, action_space, device)
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self.obs_dim = self.observation_space.shape[0]
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# Default network architecture, from stable-baselines
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if net_arch is None:
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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.adam_epsilon = adam_epsilon
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self.ortho_init = ortho_init
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self.net_args = {
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'input_dim': self.obs_dim,
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'output_dim': -1,
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'net_arch': self.net_arch,
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'activation_fn': self.activation_fn
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}
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self.shared_net = None
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self.pi_net, self.vf_net = None, None
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# In the future, feature_extractor will be replaced with a CNN
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self.features_extractor = nn.Flatten()
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self.features_dim = self.obs_dim
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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_feature_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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# 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(learning_rate)
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def reset_noise(self, n_envs: int = 1):
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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, 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, learning_rate):
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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_feature_extractor, latent_sde_dim = create_sde_feature_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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for module in [self.mlp_extractor, 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.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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self.optimizer = th.optim.Adam(self.parameters(), lr=learning_rate(1), eps=self.adam_epsilon)
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def forward(self, obs, deterministic=False):
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if not isinstance(obs, th.Tensor):
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obs = th.FloatTensor(obs).to(self.device)
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latent_pi, latent_vf, latent_sde = self._get_latent(obs)
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value = self.value_net(latent_vf)
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action, action_distribution = self._get_action_dist_from_latent(latent_pi, latent_sde=latent_sde,
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deterministic=deterministic)
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log_prob = action_distribution.log_prob(action)
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return action, value, log_prob
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def _get_latent(self, obs):
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features = self.features_extractor(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_feature_extractor is not None:
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latent_sde = self.sde_feature_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, latent_sde=None, deterministic=False):
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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, deterministic=deterministic)
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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(mean_actions, deterministic=deterministic)
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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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deterministic=deterministic)
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def actor_forward(self, obs, deterministic=False):
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latent_pi, _, latent_sde = self._get_latent(obs)
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action, _ = self._get_action_dist_from_latent(latent_pi, latent_sde, deterministic=deterministic)
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return action.detach().cpu().numpy()
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def evaluate_actions(self, obs, action, deterministic=False):
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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 action: (th.Tensor)
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:param deterministic: (bool)
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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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_, action_distribution = self._get_action_dist_from_latent(latent_pi, latent_sde, deterministic=deterministic)
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log_prob = action_distribution.log_prob(action)
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value = self.value_net(latent_vf)
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return value, log_prob, action_distribution.entropy()
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def value_forward(self, obs):
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_, latent_vf, _ = self._get_latent(obs)
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return self.value_net(latent_vf)
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MlpPolicy = PPOPolicy
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register_policy("MlpPolicy", MlpPolicy)
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