mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-09-04 20:23:54 +00:00
210 lines
8.5 KiB
Python
210 lines
8.5 KiB
Python
import torch as th
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import torch.nn as nn
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from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp, BaseNetwork
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from torchy_baselines.common.distributions import StateDependentNoiseDistribution
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class Actor(BaseNetwork):
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"""
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Actor network (policy) for TD3.
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:param obs_dim: (int) Dimension of the observation
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:param action_dim: (int) Dimension of the action space
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:param net_arch: ([int]) Network architecture
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:param activation_fn: (nn.Module) Activation function
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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 clip_noise: (float) Clip the magnitude of the noise
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:param lr_sde: (float) Learning rate for the standard deviation of the noise
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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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"""
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def __init__(self, obs_dim, action_dim, net_arch, activation_fn=nn.ReLU,
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use_sde=False, log_std_init=-3, clip_noise=None,
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lr_sde=3e-4, full_std=False):
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super(Actor, self).__init__()
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self.latent_pi, self.log_std = None, None
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self.weights_dist, self.exploration_mat = None, None
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self.use_sde, self.sde_optimizer = use_sde, None
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self.action_dim = action_dim
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self.full_std = full_std
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if use_sde:
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latent_pi = create_mlp(obs_dim, -1, net_arch, activation_fn, squash_out=False)
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self.latent_pi = nn.Sequential(*latent_pi)
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# Create state dependent noise matrix (SDE)
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self.action_dist = StateDependentNoiseDistribution(action_dim, full_std=full_std, use_expln=False,
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squash_output=False)
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action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=net_arch[-1],
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log_std_init=log_std_init)
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# Squash output
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self.actor_net = nn.Sequential(action_net, nn.Tanh())
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self.clip_noise = clip_noise
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self.sde_optimizer = th.optim.Adam([self.log_std], lr=lr_sde)
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self.reset_noise()
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else:
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actor_net = create_mlp(obs_dim, action_dim, net_arch, activation_fn, squash_out=True)
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self.actor_net = nn.Sequential(*actor_net)
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def get_std(self):
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"""
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Retrieve the standard deviation of the action distribution.
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Only useful when using SDE.
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It corresponds to `th.exp(log_std)` in the normal case,
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but is slightly different when using `expln` function
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(cf StateDependentNoiseDistribution doc).
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:return: (th.Tensor)
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"""
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return self.action_dist.get_std(self.log_std)
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def _get_action_dist_from_latent(self, latent_pi):
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mean_actions = self.actor_net(latent_pi)
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return self.action_dist.proba_distribution(mean_actions, self.log_std, latent_pi)
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def evaluate_actions(self, obs, action):
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"""
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Evaluate actions according to the current policy,
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given the observations. Only useful when using SDE.
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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) log likelihood of taking those actions
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and entropy of the action distribution.
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"""
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with th.no_grad():
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latent_pi = self.latent_pi(obs)
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_, distribution = self._get_action_dist_from_latent(latent_pi)
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log_prob = distribution.log_prob(action)
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# value = self.value_net(latent_vf)
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return log_prob, distribution.entropy()
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def reset_noise(self):
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"""
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Sample new weights for the exploration matrix, when using SDE.
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"""
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self.action_dist.sample_weights(self.log_std)
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def forward(self, obs, deterministic=True):
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if self.use_sde:
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latent_pi = self.latent_pi(obs)
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if deterministic:
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return self.actor_net(latent_pi)
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noise = self.action_dist.get_noise(latent_pi)
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if self.clip_noise is not None:
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noise = th.clamp(noise, -self.clip_noise, self.clip_noise)
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# TODO: Replace with squashing -> need to account for that in the sde update
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# -> set squash_out=True in the action_dist?
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# NOTE: the clipping is done in the rollout for now
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return self.actor_net(latent_pi) + noise
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# action, _ = self._get_action_dist_from_latent(latent_pi)
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# return action
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else:
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return self.actor_net(obs)
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class Critic(BaseNetwork):
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"""
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Critic network for TD3,
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in fact it represents the action-state value function (Q-value function)
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:param obs_dim: (int) Dimension of the observation
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:param action_dim: (int) Dimension of the action space
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:param net_arch: ([int]) Network architecture
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:param activation_fn: (nn.Module) Activation function
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"""
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def __init__(self, obs_dim, action_dim,
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net_arch, activation_fn=nn.ReLU):
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super(Critic, self).__init__()
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q1_net = create_mlp(obs_dim + action_dim, 1, net_arch, activation_fn)
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self.q1_net = nn.Sequential(*q1_net)
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q2_net = create_mlp(obs_dim + action_dim, 1, net_arch, activation_fn)
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self.q2_net = nn.Sequential(*q2_net)
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self.q_networks = [self.q1_net, self.q2_net]
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def forward(self, obs, action):
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qvalue_input = th.cat([obs, action], dim=1)
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return [q_net(qvalue_input) for q_net in self.q_networks]
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def q1_forward(self, obs, action):
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return self.q_networks[0](th.cat([obs, action], dim=1))
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class TD3Policy(BasePolicy):
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"""
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Policy class (with both actor and critic) for TD3.
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:param observation_space: (gym.spaces.Space) Observation space
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:param action_dim: (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 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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"""
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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.ReLU, use_sde=False, log_std_init=-3,
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clip_noise=None, lr_sde=3e-4):
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super(TD3Policy, self).__init__(observation_space, action_space, device)
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# Default network architecture, from the original paper
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if net_arch is None:
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net_arch = [400, 300]
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self.obs_dim = self.observation_space.shape[0]
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self.action_dim = self.action_space.shape[0]
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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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'obs_dim': self.obs_dim,
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'action_dim': self.action_dim,
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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.actor_kwargs = self.net_args.copy()
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self.actor_kwargs['use_sde'] = use_sde
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self.actor_kwargs['log_std_init'] = log_std_init
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self.actor_kwargs['clip_noise'] = clip_noise
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self.actor_kwargs['lr_sde'] = lr_sde
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self.actor, self.actor_target = None, None
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self.critic, self.critic_target = None, None
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self.use_sde = use_sde
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self.log_std_init = log_std_init
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self._build(learning_rate)
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def _build(self, learning_rate):
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self.actor = self.make_actor()
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self.actor_target = self.make_actor()
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self.actor_target.load_state_dict(self.actor.state_dict())
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self.actor.optimizer = th.optim.Adam(self.actor.parameters(), lr=learning_rate(1))
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self.critic = self.make_critic()
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self.critic_target = self.make_critic()
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self.critic_target.load_state_dict(self.critic.state_dict())
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self.critic.optimizer = th.optim.Adam(self.critic.parameters(), lr=learning_rate(1))
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def reset_noise(self):
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return self.actor.reset_noise()
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def make_actor(self):
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return Actor(**self.actor_kwargs).to(self.device)
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def make_critic(self):
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return Critic(**self.net_args).to(self.device)
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def forward(self, obs, deterministic=True):
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return self.actor(obs, deterministic=deterministic)
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MlpPolicy = TD3Policy
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register_policy("MlpPolicy", MlpPolicy)
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