"""
Deep Deterministic Policy Gradient (DDPG)
Paper link: https://arxiv.org/pdf/1509.02971.pdf
Implementation: Pytorch
"""
import torch
from torch import nn
from xuance.torch.learners import Learner
from argparse import Namespace
[docs]
class DDPG_Learner(Learner):
def __init__(self,
config: Namespace,
policy: nn.Module,
callback):
super(DDPG_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.mse_loss = nn.MSELoss()
[docs]
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 = self.policy.Qaction(obs_batch, act_batch).reshape([-1])
next_q = self.policy.Qtarget(next_batch).reshape([-1])
target_q = rew_batch + (1 - ter_batch) * self.gamma * next_q
q_loss = self.mse_loss(action_q, 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()
# actor update
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.scheduler['critic'].step()
self.policy.soft_update(self.tau)
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"Ploss/rank_{self.rank}": p_loss.item(),
f"Qvalue/rank_{self.rank}": action_q.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(),
"Ploss": p_loss.item(),
"Qvalue": action_q.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=action_q, next_q=next_q, target_q=target_q, policy_q=policy_q,
q_loss=q_loss, p_loss=p_loss))
return info