"""
DQN with Double Q-learning (Double DQN)
Paper link: https://ojs.aaai.org/index.php/AAAI/article/view/10295
Implementation: Pytorch
"""
import torch
from torch import nn
from xuance.torch.learners import Learner
from argparse import Namespace
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class DDQN_Learner(Learner):
def __init__(self,
config: Namespace,
policy: nn.Module,
callback):
super(DDQN_Learner, self).__init__(config, policy, callback)
self.optimizer = torch.optim.Adam(self.policy.parameters(), self.config.learning_rate, eps=1e-5)
self.scheduler = torch.optim.lr_scheduler.LinearLR(self.optimizer,
start_factor=1.0,
end_factor=self.end_factor_lr_decay,
total_iters=self.total_iters)
self.gamma = config.gamma
self.sync_frequency = config.sync_frequency
self.mse_loss = nn.MSELoss()
self.n_actions = self.policy.action_dim
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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'], dtype=torch.int64, 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)
_, _, evalQ = self.policy(obs_batch)
_, targetA, _ = self.policy(next_batch)
_, _, targetQ = self.policy.target(next_batch)
predictQ = evalQ.gather(-1, act_batch.unsqueeze(-1)).squeeze(-1)
targetQ = targetQ.gather(-1, targetA.unsqueeze(-1)).squeeze(-1)
targetQ = rew_batch + self.gamma * (1 - ter_batch) * targetQ
loss = self.mse_loss(predictQ, targetQ.detach())
self.optimizer.zero_grad()
loss.backward()
if self.use_grad_clip:
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), self.grad_clip_norm)
self.optimizer.step()
if self.scheduler is not None:
self.scheduler.step()
# hard update for target network
if self.iterations % self.sync_frequency == 0:
self.policy.copy_target()
lr = self.optimizer.state_dict()['param_groups'][0]['lr']
if self.distributed_training:
info.update({
f"Qloss/rank_{self.rank}": loss.item(),
f"learning_rate/rank_{self.rank}": lr,
f"predictQ/rank_{self.rank}": predictQ.mean().item()
})
else:
info.update({
"Qloss": loss.item(),
"learning_rate": lr,
"predictQ": predictQ.mean().item()
})
info.update(self.callback.on_update_end(self.iterations,
policy=self.policy, info=info,
evalQ=evalQ, targetA=targetA, targetQ=targetQ, predictQ=predictQ,
loss=loss))
return info