mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-09-04 20:23:54 +00:00
158 lines
6.1 KiB
Python
158 lines
6.1 KiB
Python
import torch as th
|
|
import torch.nn as nn
|
|
from torch.distributions import Normal
|
|
|
|
from torchy_baselines.common.policies import BasePolicy, register_policy, create_mlp, BaseNetwork
|
|
|
|
|
|
class Actor(BaseNetwork):
|
|
def __init__(self, obs_dim, action_dim, net_arch, activation_fn=nn.ReLU,
|
|
use_sde=False, log_std_init=-2, clip_noise=None,
|
|
lr_sde=3e-4, full_std=False):
|
|
super(Actor, self).__init__()
|
|
|
|
self.latent_pi, self.log_std = None, None
|
|
self.weights_dist, self.exploration_mat = None, None
|
|
self.use_sde, self.sde_optimizer = use_sde, None
|
|
self.action_dim = action_dim
|
|
self.full_std = full_std
|
|
|
|
if use_sde:
|
|
latent_dim = net_arch[-1]
|
|
latent_pi = create_mlp(obs_dim, -1, net_arch, activation_fn, squash_out=False)
|
|
self.latent_pi = nn.Sequential(*latent_pi)
|
|
if full_std:
|
|
self.log_std = nn.Parameter(th.ones(latent_dim, action_dim) * log_std_init, requires_grad=True)
|
|
else:
|
|
# Reduce the number of parameters:
|
|
self.log_std = nn.Parameter(th.ones(latent_dim, 1) * log_std_init, requires_grad=True)
|
|
|
|
self.latent_dim = latent_dim
|
|
self.actor_net = nn.Sequential(nn.Linear(net_arch[-1], action_dim), nn.Tanh())
|
|
self.clip_noise = clip_noise
|
|
self.sde_optimizer = th.optim.Adam([self.log_std], lr=lr_sde)
|
|
self.reset_noise()
|
|
else:
|
|
actor_net = create_mlp(obs_dim, action_dim, net_arch, activation_fn, squash_out=True)
|
|
self.actor_net = nn.Sequential(*actor_net)
|
|
|
|
def get_log_std(self):
|
|
if self.full_std:
|
|
return self.log_std
|
|
# Reduce the number of parameters:
|
|
return th.ones((self.latent_dim, self.action_dim)).to(self.log_std.device) * self.log_std
|
|
|
|
def evaluate_actions(self, obs, action):
|
|
with th.no_grad():
|
|
latent_pi = self.latent_pi(obs)
|
|
mean_actions = self.actor_net(latent_pi)
|
|
|
|
variance = th.mm(latent_pi ** 2, th.exp(self.get_log_std()) ** 2)
|
|
distribution = Normal(mean_actions, th.sqrt(variance + 1e-5))
|
|
log_prob = distribution.log_prob(action)
|
|
if len(log_prob.shape) > 1:
|
|
log_prob = log_prob.sum(axis=1)
|
|
else:
|
|
log_prob = log_prob.sum()
|
|
return log_prob, distribution.entropy()
|
|
|
|
def reset_noise(self):
|
|
self.weights_dist = Normal(th.zeros_like(self.get_log_std()), th.exp(self.get_log_std()))
|
|
self.exploration_mat = self.weights_dist.rsample()
|
|
|
|
def forward(self, obs, deterministic=True):
|
|
if self.use_sde:
|
|
latent_pi = self.latent_pi(obs)
|
|
if deterministic:
|
|
return self.actor_net(latent_pi)
|
|
noise = th.mm(latent_pi.detach(), self.exploration_mat)
|
|
if self.clip_noise is not None:
|
|
noise = th.clamp(noise, -self.clip_noise, self.clip_noise)
|
|
# TODO: Replace with squashing -> need to account for that in the sde update
|
|
# return th.clamp(self.actor_net(latent_pi) + noise, -1, 1)
|
|
# NOTE: the clipping is done in the rollout for now
|
|
return self.actor_net(latent_pi) + noise
|
|
else:
|
|
return self.actor_net(obs)
|
|
|
|
|
|
class Critic(BaseNetwork):
|
|
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 TD3Policy(BasePolicy):
|
|
def __init__(self, observation_space, action_space,
|
|
learning_rate, net_arch=None, device='cpu',
|
|
activation_fn=nn.ReLU, use_sde=False, log_std_init=-2,
|
|
clip_noise=None, lr_sde=3e-4):
|
|
super(TD3Policy, self).__init__(observation_space, action_space, device)
|
|
|
|
if net_arch is None:
|
|
net_arch = [400, 300]
|
|
|
|
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()
|
|
self.actor_kwargs['use_sde'] = use_sde
|
|
self.actor_kwargs['log_std_init'] = log_std_init
|
|
self.actor_kwargs['clip_noise'] = clip_noise
|
|
self.actor_kwargs['lr_sde'] = lr_sde
|
|
|
|
self.actor, self.actor_target = None, None
|
|
self.critic, self.critic_target = None, None
|
|
self.use_sde = use_sde
|
|
self.log_std_init = log_std_init
|
|
self._build(learning_rate)
|
|
|
|
def _build(self, learning_rate):
|
|
self.actor = self.make_actor()
|
|
self.actor_target = self.make_actor()
|
|
self.actor_target.load_state_dict(self.actor.state_dict())
|
|
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 reset_noise(self):
|
|
return self.actor.reset_noise()
|
|
|
|
def make_actor(self):
|
|
return Actor(**self.actor_kwargs).to(self.device)
|
|
|
|
def make_critic(self):
|
|
return Critic(**self.net_args).to(self.device)
|
|
|
|
def forward(self, obs, deterministic=True):
|
|
return self.actor(obs, deterministic=deterministic)
|
|
|
|
|
|
MlpPolicy = TD3Policy
|
|
|
|
register_policy("MlpPolicy", MlpPolicy)
|