99abcc6e9b57f441999ce10dbc31ca1bed79c356,ch15/04_train_ppo.py,,,#,55

Before Change

                surr_obj_v = adv_v * torch.exp(logprob_pi_v - logprob_old_pi_v)
                clipped_surr_v = torch.clamp(surr_obj_v, 1.0 - PPO_EPS, 1.0 + PPO_EPS)
                loss_policy_v = -torch.min(surr_obj_v, clipped_surr_v).mean()
                entropy_loss_v = ENTROPY_BETA * (-(torch.log(2*math.pi*var_v) + 1)/2).mean()

                loss_v = loss_policy_v + entropy_loss_v + loss_value_v
                loss_v.backward()
                optimizer.step()

After Change

        net_act.cuda()
        net_crt.cuda()
    print(net_act)
    print(net_crt)

    writer = SummaryWriter(comment="-ppo_" + args.name)
    agent = model.AgentA2C(net_act, cuda=args.cuda)
    exp_source = ptan.experience.ExperienceSourceFirstLast(envs, agent, GAMMA, steps_count=REWARD_STEPS)

Instances