"""
Twin Delayed Deep Deterministic Policy Gradient (TD3)
Paper link: http://proceedings.mlr.press/v80/fujimoto18a/fujimoto18a.pdf
Implementation: Pytorch
"""
import torch
from torch import nn
from xuance.torch.learners import Learner
from argparse import Namespace
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class TD3_Learner(Learner):
def __init__(self,
config: Namespace,
policy: nn.Module,
callback):
super(TD3_Learner, self).__init__(config, policy, callback)
self.optimizer = {
'actor': torch.optim.Adam(self.policy.actor_parameters, self.config.learning_rate_actor),
'critic': torch.optim.Adam(self.policy.critic_parameters, self.config.learning_rate_critic)}
self.scheduler = {
'actor': torch.optim.lr_scheduler.LinearLR(self.optimizer['actor'],
start_factor=1.0,
end_factor=self.end_factor_lr_decay,
total_iters=self.total_iters),
'critic': torch.optim.lr_scheduler.LinearLR(self.optimizer['critic'],
start_factor=1.0,
end_factor=self.end_factor_lr_decay,
total_iters=self.total_iters)}
self.tau = config.tau
self.gamma = config.gamma
self.actor_update_delay = config.actor_update_delay
self.mse_loss = nn.MSELoss()
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def update(self, **samples):
self.iterations += 1
obs_batch = torch.as_tensor(samples['obs'], device=self.device)
act_batch = torch.as_tensor(samples['actions'], device=self.device)
next_batch = torch.as_tensor(samples['obs_next'], device=self.device)
rew_batch = torch.as_tensor(samples['rewards'], device=self.device)
ter_batch = torch.as_tensor(samples['terminals'], dtype=torch.float, device=self.device)
info = self.callback.on_update_start(self.iterations,
policy=self.policy, obs=obs_batch, act=act_batch,
next_obs=next_batch, rew=rew_batch, termination=ter_batch)
# critic update
action_q_A, action_q_B = self.policy.Qaction(obs_batch, act_batch)
action_q_A = action_q_A.reshape([-1])
action_q_B = action_q_B.reshape([-1])
next_q = self.policy.Qtarget(next_batch).reshape([-1])
target_q = rew_batch + self.gamma * (1 - ter_batch) * next_q
q_loss = self.mse_loss(action_q_A, target_q.detach()) + self.mse_loss(action_q_B, target_q.detach())
self.optimizer['critic'].zero_grad()
q_loss.backward()
if self.use_grad_clip:
torch.nn.utils.clip_grad_norm_(self.policy.critic_parameters, self.grad_clip_norm)
self.optimizer['critic'].step()
if self.scheduler is not None:
self.scheduler['critic'].step()
# actor update
policy_q, p_loss = None, None
if self.iterations % self.actor_update_delay == 0:
policy_q = self.policy.Qpolicy(obs_batch)
p_loss = -policy_q.mean()
self.optimizer['actor'].zero_grad()
p_loss.backward()
if self.use_grad_clip:
torch.nn.utils.clip_grad_norm_(self.policy.actor_parameters, self.grad_clip_norm)
self.optimizer['actor'].step()
if self.scheduler is not None:
self.scheduler['actor'].step()
self.policy.soft_update(self.tau)
info.update({"Ploss": p_loss.item()})
actor_lr = self.optimizer['actor'].state_dict()['param_groups'][0]['lr']
critic_lr = self.optimizer['critic'].state_dict()['param_groups'][0]['lr']
if self.distributed_training:
info.update({
f"Qloss/rank_{self.rank}": q_loss.item(),
f"QvalueA/rank_{self.rank}": action_q_A.mean().item(),
f"QvalueB/rank_{self.rank}": action_q_B.mean().item(),
f"actor_lr/rank_{self.rank}": actor_lr,
f"critic_lr/rank_{self.rank}": critic_lr
})
else:
info.update({
"Qloss": q_loss.item(),
"QvalueA": action_q_A.mean().item(),
"QvalueB": action_q_B.mean().item(),
"actor_lr": actor_lr,
"critic_lr": critic_lr
})
info.update(self.callback.on_update_end(self.iterations,
policy=self.policy, info=info,
action_q_A=action_q_A, action_q_B=action_q_B,
next_q=next_q, target_q=target_q, q_loss=q_loss,
policy_q=policy_q, p_loss=p_loss))
return info