64 lines
2.4 KiB
Python
64 lines
2.4 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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from DDPG.env import OUNoise
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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}，设备：{cfg.device}')
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ou_noise = OUNoise(env.action_space) # 动作噪声
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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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ou_noise.reset()
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done = False
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ep_reward = 0
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i_step = 0
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while not done:
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i_step += 1
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action = agent.choose_action(state)
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action = ou_noise.get_action(action, i_step)
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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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agent.update()
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state = next_state
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if (i_ep+1)%10 == 0:
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print('回合：{}/{}，奖励：{:.2f}'.format(i_ep+1, cfg.train_eps, ep_reward))
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(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}, 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.eval_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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i_step = 0
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while not done:
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i_step += 1
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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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state = next_state
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print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, ep_reward))
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(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(f"回合：{i_ep+1}/{cfg.eval_eps}，奖励：{ep_reward:.1f}")
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print('完成测试！')
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return rewards, ma_rewards |