77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
import sys
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import os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
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parent_path = os.path.dirname(curr_path) # 父路径
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sys.path.append(parent_path) # 添加路径到系统路径
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import numpy as np
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def train(cfg, env, agent):
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print('开始训练!')
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print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
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rewards = [] # 记录所有回合的奖励
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ma_rewards = [] # 记录所有回合的滑动平均奖励
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for i_ep in range(cfg.train_eps):
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state = env.reset()
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done = False
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ep_reward = 0
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while not done:
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goal = agent.set_goal(state)
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onehot_goal = agent.to_onehot(goal)
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meta_state = state
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extrinsic_reward = 0
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while not done and goal != np.argmax(state):
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goal_state = np.concatenate([state, onehot_goal])
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action = agent.choose_action(goal_state)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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extrinsic_reward += reward
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intrinsic_reward = 1.0 if goal == np.argmax(
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next_state) else 0.0
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agent.memory.push(goal_state, action, intrinsic_reward, np.concatenate(
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[next_state, onehot_goal]), done)
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state = next_state
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agent.update()
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if (i_ep+1)%10 == 0:
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print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}，Loss:{agent.loss_numpy:.2f}， Meta_Loss:{agent.meta_loss_numpy:.2f}')
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agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(
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0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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print('完成训练！')
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return rewards, ma_rewards
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def test(cfg, env, agent):
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print('开始测试!')
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print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
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rewards = [] # 记录所有回合的奖励
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ma_rewards = [] # 记录所有回合的滑动平均奖励
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for i_ep in range(cfg.train_eps):
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state = env.reset()
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done = False
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ep_reward = 0
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while not done:
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goal = agent.set_goal(state)
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onehot_goal = agent.to_onehot(goal)
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extrinsic_reward = 0
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while not done and goal != np.argmax(state):
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goal_state = np.concatenate([state, onehot_goal])
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action = agent.choose_action(goal_state)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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extrinsic_reward += reward
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state = next_state
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agent.update()
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if (i_ep+1)%10 == 0:
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print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}，Loss:{agent.loss_numpy:.2f}， Meta_Loss:{agent.meta_loss_numpy:.2f}')
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(
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0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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print('完成训练！')
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return rewards, ma_rewards |