74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: JiangJi
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Email: johnjim0816@gmail.com
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Date: 2021-11-07 18:10:37
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LastEditor: JiangJi
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LastEditTime: 2021-11-19 18:34:05
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Discription:
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'''
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import sys,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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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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ep_reward = 0 # 记录一回合内的奖励
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state = env.reset() # 重置环境，返回初始状态
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while True:
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action = agent.choose_action(state)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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agent.memory.push(state, action, reward, next_state, done)
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state = next_state
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agent.update()
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if done:
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break
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if i_ep % cfg.target_update == 0:
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agent.target_net.load_state_dict(agent.policy_net.state_dict())
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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}')
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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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# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0
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cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon
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cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon
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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.test_eps):
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state = env.reset()
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ep_reward = 0
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while True:
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action = agent.choose_action(state)
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next_state, reward, done, _ = env.step(action)
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state = next_state
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ep_reward += reward
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if done:
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break
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
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else:
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ma_rewards.append(ep_reward)
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print(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
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print('完成测试！')
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return rewards,ma_rewards
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