mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-09-05 20:30:42 +00:00
239 lines
9.7 KiB
Python
239 lines
9.7 KiB
Python
import torch as th
|
|
import torch.nn as nn
|
|
|
|
from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp, BaseNetwork, \
|
|
create_sde_feature_extractor
|
|
from torchy_baselines.common.distributions import SquashedDiagGaussianDistribution, StateDependentNoiseDistribution
|
|
|
|
# CAP the standard deviation of the actor
|
|
LOG_STD_MAX = 2
|
|
LOG_STD_MIN = -20
|
|
|
|
|
|
class LeakyClip(nn.Module):
|
|
"""
|
|
Cip values outside a certain range
|
|
(it is not a hard clip, there is a small slope to have non-zero gradient)
|
|
|
|
:param min_val: (float)
|
|
:param max_val: (float)
|
|
:param slope: (float)
|
|
"""
|
|
def __init__(self, min_val=-2.0, max_val=2.0, slope=0.01):
|
|
super(LeakyClip, self).__init__()
|
|
self.min_val = min_val
|
|
self.max_val = max_val
|
|
self.slope = slope
|
|
|
|
def forward(self, x):
|
|
linear_part = x * (x >= self.min_val) * (x <= self.max_val)
|
|
above_max_val = self.slope * (x - self.max_val) * (x > self.max_val)
|
|
below_min_val = self.slope * (x - self.min_val) * (x < self.min_val)
|
|
return linear_part + below_min_val + above_max_val
|
|
|
|
|
|
class Actor(BaseNetwork):
|
|
"""
|
|
Actor network (policy) for SAC.
|
|
|
|
:param obs_dim: (int) Dimension of the observation
|
|
:param action_dim: (int) Dimension of the action space
|
|
:param net_arch: ([int]) Network architecture
|
|
:param activation_fn: (nn.Module) Activation function
|
|
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
|
:param log_std_init: (float) Initial value for the log standard deviation
|
|
:param full_std: (bool) Whether to use (n_features x n_actions) parameters
|
|
for the std instead of only (n_features,) when using SDE.
|
|
:param sde_net_arch: ([int]) Network architecture for extracting features
|
|
when using SDE. If None, the latent features from the policy will be used.
|
|
Pass an empty list to use the states as features.
|
|
"""
|
|
def __init__(self, obs_dim, action_dim, net_arch, activation_fn=nn.ReLU,
|
|
use_sde=False, log_std_init=-3, full_std=True, sde_net_arch=None):
|
|
super(Actor, self).__init__()
|
|
|
|
latent_pi_net = create_mlp(obs_dim, -1, net_arch, activation_fn)
|
|
self.latent_pi = nn.Sequential(*latent_pi_net)
|
|
self.use_sde = use_sde
|
|
self.sde_feature_extractor = None
|
|
|
|
if self.use_sde:
|
|
latent_sde_dim = net_arch[-1]
|
|
# Separate feature extractor for SDE
|
|
if sde_net_arch is not None:
|
|
self.sde_feature_extractor, latent_sde_dim = create_sde_feature_extractor(obs_dim, sde_net_arch,
|
|
activation_fn)
|
|
|
|
# TODO: check for the learn_features
|
|
self.action_dist = StateDependentNoiseDistribution(action_dim, full_std=full_std, use_expln=False,
|
|
learn_features=True, squash_output=True)
|
|
self.mu, self.log_std = self.action_dist.proba_distribution_net(latent_dim=net_arch[-1],
|
|
latent_sde_dim=latent_sde_dim,
|
|
log_std_init=log_std_init)
|
|
# Avoid saturation by limiting the mean of the gaussian to be in [-1, 1]
|
|
# self.mu = nn.Sequential(self.mu, nn.Tanh())
|
|
self.mu = nn.Sequential(self.mu, nn.Hardtanh(min_val=-2.0, max_val=2.0))
|
|
# Small positive slope to have non-zero gradient
|
|
self.mu = nn.Sequential(self.mu, LeakyClip())
|
|
else:
|
|
self.action_dist = SquashedDiagGaussianDistribution(action_dim)
|
|
self.mu = nn.Linear(net_arch[-1], action_dim)
|
|
self.log_std = nn.Linear(net_arch[-1], action_dim)
|
|
|
|
def get_std(self):
|
|
"""
|
|
Retrieve the standard deviation of the action distribution.
|
|
Only useful when using SDE.
|
|
It corresponds to `th.exp(log_std)` in the normal case,
|
|
but is slightly different when using `expln` function
|
|
(cf StateDependentNoiseDistribution doc).
|
|
|
|
:return: (th.Tensor)
|
|
"""
|
|
return self.action_dist.get_std(self.log_std)
|
|
|
|
def reset_noise(self, batch_size=1):
|
|
"""
|
|
Sample new weights for the exploration matrix, when using SDE.
|
|
|
|
:param batch_size: (int)
|
|
"""
|
|
self.action_dist.sample_weights(self.log_std, batch_size=batch_size)
|
|
|
|
def _get_latent(self, obs):
|
|
latent_pi = self.latent_pi(obs)
|
|
|
|
if self.sde_feature_extractor is not None:
|
|
latent_sde = self.sde_feature_extractor(obs)
|
|
else:
|
|
latent_sde = latent_pi
|
|
return latent_pi, latent_sde
|
|
|
|
def get_action_dist_params(self, obs):
|
|
latent_pi, latent_sde = self._get_latent(obs)
|
|
|
|
if self.use_sde:
|
|
mean_actions, log_std = self.mu(latent_pi), self.log_std
|
|
else:
|
|
mean_actions, log_std = self.mu(latent_pi), self.log_std(latent_pi)
|
|
# Original Implementation to cap the standard deviation
|
|
log_std = th.clamp(log_std, LOG_STD_MIN, LOG_STD_MAX)
|
|
return mean_actions, log_std, latent_sde
|
|
|
|
def forward(self, obs, deterministic=False):
|
|
mean_actions, log_std, latent_sde = self.get_action_dist_params(obs)
|
|
if self.use_sde:
|
|
# Note the action is squashed
|
|
action, _ = self.action_dist.proba_distribution(mean_actions, log_std, latent_sde,
|
|
deterministic=deterministic)
|
|
else:
|
|
# Note the action is squashed
|
|
action, _ = self.action_dist.proba_distribution(mean_actions, log_std,
|
|
deterministic=deterministic)
|
|
return action
|
|
|
|
def action_log_prob(self, obs):
|
|
mean_actions, log_std, latent_sde = self.get_action_dist_params(obs)
|
|
|
|
if self.use_sde:
|
|
action, log_prob = self.action_dist.log_prob_from_params(mean_actions, self.log_std, latent_sde)
|
|
else:
|
|
action, log_prob = self.action_dist.log_prob_from_params(mean_actions, log_std)
|
|
return action, log_prob
|
|
|
|
|
|
class Critic(BaseNetwork):
|
|
"""
|
|
Critic network (q-value function) for SAC.
|
|
|
|
:param obs_dim: (int) Dimension of the observation
|
|
:param action_dim: (int) Dimension of the action space
|
|
:param net_arch: ([int]) Network architecture
|
|
:param activation_fn: (nn.Module) Activation function
|
|
"""
|
|
def __init__(self, obs_dim, action_dim,
|
|
net_arch, activation_fn=nn.ReLU):
|
|
super(Critic, self).__init__()
|
|
|
|
q1_net = create_mlp(obs_dim + action_dim, 1, net_arch, activation_fn)
|
|
self.q1_net = nn.Sequential(*q1_net)
|
|
|
|
q2_net = create_mlp(obs_dim + action_dim, 1, net_arch, activation_fn)
|
|
self.q2_net = nn.Sequential(*q2_net)
|
|
|
|
self.q_networks = [self.q1_net, self.q2_net]
|
|
|
|
def forward(self, obs, action):
|
|
qvalue_input = th.cat([obs, action], dim=1)
|
|
return [q_net(qvalue_input) for q_net in self.q_networks]
|
|
|
|
def q1_forward(self, obs, action):
|
|
return self.q_networks[0](th.cat([obs, action], dim=1))
|
|
|
|
|
|
class SACPolicy(BasePolicy):
|
|
"""
|
|
Policy class (with both actor and critic) for SAC.
|
|
|
|
:param observation_space: (gym.spaces.Space) Observation space
|
|
:param action_space: (gym.spaces.Space) Action space
|
|
:param learning_rate: (callable) Learning rate schedule (could be constant)
|
|
:param net_arch: ([int or dict]) The specification of the policy and value networks.
|
|
:param device: (str or th.device) Device on which the code should run.
|
|
:param activation_fn: (nn.Module) Activation function
|
|
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
|
:param log_std_init: (float) Initial value for the log standard deviation
|
|
:param sde_net_arch: ([int]) Network architecture for extracting features
|
|
when using SDE. If None, the latent features from the policy will be used.
|
|
Pass an empty list to use the states as features.
|
|
"""
|
|
def __init__(self, observation_space, action_space,
|
|
learning_rate, net_arch=None, device='cpu',
|
|
activation_fn=nn.ReLU, use_sde=False,
|
|
log_std_init=-3, sde_net_arch=None):
|
|
super(SACPolicy, self).__init__(observation_space, action_space, device)
|
|
|
|
if net_arch is None:
|
|
net_arch = [256, 256]
|
|
|
|
self.obs_dim = self.observation_space.shape[0]
|
|
self.action_dim = self.action_space.shape[0]
|
|
self.net_arch = net_arch
|
|
self.activation_fn = activation_fn
|
|
self.net_args = {
|
|
'obs_dim': self.obs_dim,
|
|
'action_dim': self.action_dim,
|
|
'net_arch': self.net_arch,
|
|
'activation_fn': self.activation_fn
|
|
}
|
|
self.actor_kwargs = self.net_args.copy()
|
|
sde_kwargs = {
|
|
'use_sde': use_sde,
|
|
'log_std_init': log_std_init,
|
|
'sde_net_arch': sde_net_arch
|
|
}
|
|
self.actor_kwargs.update(sde_kwargs)
|
|
self.actor, self.actor_target = None, None
|
|
self.critic, self.critic_target = None, None
|
|
|
|
self._build(learning_rate)
|
|
|
|
def _build(self, learning_rate):
|
|
self.actor = self.make_actor()
|
|
self.actor.optimizer = th.optim.Adam(self.actor.parameters(), lr=learning_rate(1))
|
|
|
|
self.critic = self.make_critic()
|
|
self.critic_target = self.make_critic()
|
|
self.critic_target.load_state_dict(self.critic.state_dict())
|
|
self.critic.optimizer = th.optim.Adam(self.critic.parameters(), lr=learning_rate(1))
|
|
|
|
def make_actor(self):
|
|
return Actor(**self.actor_kwargs).to(self.device)
|
|
|
|
def make_critic(self):
|
|
return Critic(**self.net_args).to(self.device)
|
|
|
|
|
|
MlpPolicy = SACPolicy
|
|
|
|
register_policy("MlpPolicy", MlpPolicy)
|