"""
Parameterised deep Q network (P-DQN)
Paper link: https://arxiv.org/pdf/1810.06394.pdf
Implementation: Pytorch
"""
import torch
from torch import nn
from xuance.torch.learners import Learner
from argparse import Namespace
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class PDQN_Learner(Learner):
def __init__(self,
config: Namespace,
policy: nn.Module,
callback):
super(PDQN_Learner, self).__init__(config, policy, callback)
conactor_optimizer = torch.optim.Adam(self.policy.conactor.parameters(), self.config.learning_rate)
qnetwork_optimizer = torch.optim.Adam(self.policy.qnetwork.parameters(), self.config.learning_rate)
self.optimizers = [conactor_optimizer, qnetwork_optimizer]
conactor_lr_scheduler = torch.optim.lr_scheduler.LinearLR(conactor_optimizer,
start_factor=1.0,
end_factor=self.end_factor_lr_decay,
total_iters=self.total_iters)
qnetwork_lr_scheduler = torch.optim.lr_scheduler.LinearLR(qnetwork_optimizer,
start_factor=1.0,
end_factor=self.end_factor_lr_decay,
total_iters=self.total_iters)
self.scheduler = [conactor_lr_scheduler, qnetwork_lr_scheduler]
self.tau = config.tau
self.gamma = config.gamma
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)
hyact_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)
disact_batch = hyact_batch[:, 0].long()
conact_batch = hyact_batch[:, 1:]
info = self.callback.on_update_start(self.iterations,
policy=self.policy, obs=obs_batch, hyact_batch=hyact_batch,
disact_batch=disact_batch, conact_batch=conact_batch,
next_obs=next_batch, rew=rew_batch, termination=ter_batch)
# optimize Q-network
with torch.no_grad():
target_conact = self.policy.Atarget(next_batch)
target_q = self.policy.Qtarget(next_batch, target_conact)
target_q = torch.max(target_q, 1, keepdim=True)[0].squeeze()
target_q = rew_batch + (1 - ter_batch) * self.gamma * target_q
eval_qs = self.policy.Qeval(obs_batch, conact_batch)
eval_q = eval_qs.gather(1, disact_batch.view(-1, 1)).squeeze()
q_loss = self.mse_loss(eval_q, target_q)
self.optimizer[1].zero_grad()
q_loss.backward()
self.optimizer[1].step()
# optimize actor network
policy_q = self.policy.Qpolicy(obs_batch)
p_loss = - policy_q.mean()
self.optimizer[0].zero_grad()
p_loss.backward()
self.optimizer[0].step()
if self.scheduler is not None:
self.scheduler[0].step()
self.scheduler[1].step()
self.policy.soft_update(self.tau)
if self.distributed_training:
info.update({
f"Q_loss/rank_{self.rank}": q_loss.item(),
f"Qvalue/rank_{self.rank}": eval_q.mean().item(),
f"P_loss/rank_{self.rank}": q_loss.item()
})
else:
info.update({
"Q_loss": q_loss.item(),
"Qvalue": eval_q.mean().item(),
"P_loss": q_loss.item()
})
info.update(self.callback.on_update_end(self.iterations,
policy=self.policy, scheduler=self.scheduler, info=info,
target_conact=target_conact, target_q=target_q,
eval_qs=eval_qs, eval_q=eval_q, policy_q=policy_q,
q_loss=q_loss, p_loss=p_loss))
return info