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if t % 500 == 0:
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wandb.log({"Current Epsilon": cur_epsilon}, step=t * args.num_processes)
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# Reset linear layer
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if args.reset_interval > 0 and t % args.reset_interval == 0:
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print(f"Resetting at step {t}")
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for name, module in agent.Q.named_children():
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for keyword in ["linear", "fc"]:
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if keyword in name:
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init_(module)
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agent.Q_target = copy.deepcopy(agent.Q)
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if t % 500 and not args.qrdqn or args.c51:
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advantages = agent.advantage(state, epsilon(t))
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mean_max_advantage = advantages.max(1)[0].mean()
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mean_min_advantage = advantages.min(1)[0].mean()
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wandb.log(
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{
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"Mean Max Advantage": mean_max_advantage,
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"Mean Min Advantage": mean_min_advantage,
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},
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step=t * args.num_processes,
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)
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# Perform action and log results
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next_state, reward, done, infos = envs.step(action)
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masks = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done])
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for i, info in enumerate(infos):
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if "bad_transition" in info.keys():
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print("Bad transition")
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if level_sampler:
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if expect_new_seed[i]:
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level_seed = info["level_seed"]
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level_seeds[i][0] = level_seed
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if args.log_per_seed_stats:
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new_episode(value, estimates, level_seed, i, step=t * args.num_processes)
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expect_new_seed[i] = False
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state_deque[i].append(state[i])
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reward_deque[i].append(reward[i])
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action_deque[i].append(action[i])
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reward_stats_deque[i].append(reward[i])
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if len(state_deque[i]) == args.multi_step or done[i]:
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temp_reward = reward_deque[i]
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# if args.reward_clip > 0:
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# temp_reward = np.clip(temp_reward, -args.reward_clip, args.reward_clip)
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n_reward = multi_step_reward(temp_reward, args.gamma)
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n_state = state_deque[i][0]
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n_action = action_deque[i][0]
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replay_buffer.add(
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n_state,
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n_action,
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next_state[i],
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n_reward,
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np.uint8(done[i]),
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level_seeds[i],
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)
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if done[i]:
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reward_deque_i = list(reward_deque[i])
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for j in range(1, len(reward_deque_i)):
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n_reward = multi_step_reward(reward_deque_i[j:], args.gamma)
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n_state = state_deque[i][j]
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n_action = action_deque[i][j]
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replay_buffer.add(
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n_state,
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n_action,
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next_state[i],
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n_reward,
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np.uint8(done[i]),
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level_seeds[i],
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)
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expect_new_seed[i] = True
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if "episode" in info.keys():
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episode_reward = info["episode"]["r"]
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ppo_normalised_reward = ppo_normalise_reward(episode_reward, args.env_name)
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min_max_normalised_reward = min_max_normalise_reward(episode_reward, args.env_name)
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wandb.log(
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{
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"Train Episode Returns": episode_reward,
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"Train Episode Returns (normalised)": ppo_normalised_reward,
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"Train Episode Returns (ppo normalised)": ppo_normalised_reward,
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"Train Episode Returns (min-max normalised)": min_max_normalised_reward,
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},
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step=t * args.num_processes,
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)
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state_deque[i].clear()
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reward_deque[i].clear()
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action_deque[i].clear()
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if args.log_per_seed_stats:
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plot_level_returns(
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level_seeds,
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returns,
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estimates,
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gaps,
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episode_reward,
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i,
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step=t * args.num_processes,
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)
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if args.autodrq:
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