mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-09-04 20:23:54 +00:00
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
import torch as th
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import torch.nn as nn
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from torch.distributions import Normal
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from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp
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class PPOPolicy(BasePolicy):
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def __init__(self, observation_space, action_space,
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learning_rate=1e-3, net_arch=None, device='cpu',
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activation_fn=nn.Tanh):
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super(PPOPolicy, self).__init__(observation_space, action_space, device)
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self.state_dim = self.observation_space.shape[0]
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self.action_dim = self.action_space.shape[0]
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if net_arch is None:
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net_arch = [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.net_args = {
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'input_dim': self.state_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._build(learning_rate)
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@staticmethod
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def init_weights(module):
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if type(module) == nn.Linear:
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nn.init.orthogonal_(module.weight, gain=1)
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module.bias.data.fill_(0.0)
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def _build(self, learning_rate):
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shared_net = create_mlp(self.state_dim, output_dim=-1, net_arch=self.net_arch, activation_fn=self.activation_fn)
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self.shared_net = nn.Sequential(*shared_net).to(self.device)
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self.actor_net = nn.Linear(self.net_arch[-1], self.action_dim)
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self.value_net = nn.Linear(self.net_arch[-1], 1)
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self.log_std = nn.Parameter(th.zeros(self.action_dim))
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# Init weights:
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for module in [self.shared_net, self.actor_net, self.value_net]:
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module.apply(self.init_weights)
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self.optimizer = th.optim.Adam(self.parameters(), lr=learning_rate)
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def forward(self, state, deterministic=False):
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state = th.FloatTensor(state).to(self.device)
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latent = self.shared_net(state)
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value = self.value_net(latent)
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action, action_distribution = self._get_action_dist_from_latent(latent, deterministic=deterministic)
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log_prob = self._get_log_prob(action_distribution, action)
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return action, value, log_prob
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def _get_action_dist_from_latent(self, latent, deterministic=False):
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mean_actions = self.actor_net(latent)
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action_std = th.ones(mean_actions.size()) * self.log_std.exp()
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action_distribution = Normal(mean_actions, action_std)
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# Sample from the gaussian
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if deterministic:
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action = mean_actions
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else:
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action = action_distribution.rsample()
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return action, action_distribution
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@staticmethod
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def _get_log_prob(action_distribution, action):
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log_prob = action_distribution.log_prob(action)
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if len(log_prob.shape) > 1:
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log_prob = log_prob.sum(axis=1)
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else:
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log_prob = log_prob.sum()
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return log_prob
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def actor_forward(self, state, deterministic=False):
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state = th.FloatTensor(state).to(self.device)
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latent = self.shared_net(state)
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action, _ = self._get_action_dist_from_latent(latent, deterministic=deterministic)
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return action.detach().cpu().numpy()
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def get_policy_stats(self, state, action):
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state = th.FloatTensor(state).to(self.device)
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latent = self.shared_net(state)
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_, action_distribution = self._get_action_dist_from_latent(latent)
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log_prob = self._get_log_prob(action_distribution, action)
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value = self.value_net(latent)
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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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