mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-09-04 20:23:54 +00:00
111 lines
5 KiB
Python
111 lines
5 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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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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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, log_std_init=0.0):
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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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if net_arch is None:
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net_arch = [dict(pi=[64], vf=[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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# Action distribution
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self.action_dist = make_proba_distribution(action_space, use_sde=use_sde)
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self._build(learning_rate)
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def reset_noise_net(self):
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self.action_dist.sample_weights(self.log_std)
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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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if isinstance(self.action_dist, (DiagGaussianDistribution, StateDependentNoiseDistribution)):
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self.action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=self.mlp_extractor.latent_dim_pi,
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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=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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for module in [self.mlp_extractor, self.action_net, self.value_net]:
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# Values from stable-baselines 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 = 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, 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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return self.mlp_extractor(self.features_extractor(obs))
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def _get_action_dist_from_latent(self, latent_pi, 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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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_pi, deterministic=deterministic)
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def actor_forward(self, obs, deterministic=False):
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latent_pi, _ = self._get_latent(obs)
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action, _ = self._get_action_dist_from_latent(latent_pi, 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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latent_pi, latent_vf = self._get_latent(obs)
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_, action_distribution = self._get_action_dist_from_latent(latent_pi, 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):
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pass
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MlpPolicy = PPOPolicy
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register_policy("MlpPolicy", MlpPolicy)
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