74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
Author: JiangJi
|
||
Email: johnjim0816@gmail.com
|
||
Date: 2021-11-07 18:10:37
|
||
LastEditor: JiangJi
|
||
LastEditTime: 2021-11-19 18:34:05
|
||
Discription:
|
||
'''
|
||
|
||
import sys,os
|
||
curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
|
||
parent_path = os.path.dirname(curr_path) # 父路径
|
||
sys.path.append(parent_path) # 添加路径到系统路径
|
||
|
||
def train(cfg,env,agent):
|
||
print('开始训练!')
|
||
print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
|
||
rewards = [] # 记录所有回合的奖励
|
||
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
||
for i_ep in range(cfg.train_eps):
|
||
ep_reward = 0 # 记录一回合内的奖励
|
||
state = env.reset() # 重置环境，返回初始状态
|
||
while True:
|
||
action = agent.choose_action(state)
|
||
next_state, reward, done, _ = env.step(action)
|
||
ep_reward += reward
|
||
agent.memory.push(state, action, reward, next_state, done)
|
||
state = next_state
|
||
agent.update()
|
||
if done:
|
||
break
|
||
if i_ep % cfg.target_update == 0:
|
||
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
||
if (i_ep+1)%10 == 0:
|
||
print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}')
|
||
rewards.append(ep_reward)
|
||
if ma_rewards:
|
||
ma_rewards.append(
|
||
0.9*ma_rewards[-1]+0.1*ep_reward)
|
||
else:
|
||
ma_rewards.append(ep_reward)
|
||
print('完成训练！')
|
||
return rewards,ma_rewards
|
||
|
||
def test(cfg,env,agent):
|
||
print('开始测试!')
|
||
print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
|
||
# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0
|
||
cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon
|
||
cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon
|
||
rewards = [] # 记录所有回合的奖励
|
||
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
||
for i_ep in range(cfg.test_eps):
|
||
state = env.reset()
|
||
ep_reward = 0
|
||
while True:
|
||
action = agent.choose_action(state)
|
||
next_state, reward, done, _ = env.step(action)
|
||
state = next_state
|
||
ep_reward += reward
|
||
if done:
|
||
break
|
||
rewards.append(ep_reward)
|
||
if ma_rewards:
|
||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||
else:
|
||
ma_rewards.append(ep_reward)
|
||
print(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
|
||
print('完成测试！')
|
||
return rewards,ma_rewards
|
||
|