Thanks albanD~
I find a way that it won’t pop up the issue by rearranging the code like the following bellow:
value_loss = 0.5 * mse_loss(imaginated_values, lambda_target_values.detach())
value_optimizer.zero_grad()

action_loss = -1*(lambda_target_values.mean())
action_optimizer.zero_grad()

value_loss.backward(retain_graph=True)
action_loss.backward()

clip_grad_norm_(value_model.parameters(), clip_grad_norm)
clip_grad_norm_(action_model.parameters(), clip_grad_norm)

value_optimizer.step()
action_optimizer.step()

It won’t get the issue of  missing weights again. Thanks~