142 lines
5.8 KiB
Python
142 lines
5.8 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2020-09-11 23:03:00
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LastEditor: John
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LastEditTime: 2022-06-21 19:36:05
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Discription:
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Environment:
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'''
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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 gym
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import torch
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import datetime
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from env.gridworld_env import CliffWalkingWapper
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from qlearning import QLearning
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from common.utils import plot_rewards
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from common.utils import save_results,make_dir
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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class Config:
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'''超参数
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'''
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def __init__(self):
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################################## 环境超参数 ###################################
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self.algo_name = 'Q-learning' # 算法名称
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self.env_name = 'CliffWalking-v0' # 环境名称
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # 检测GPUgjgjlkhfsf风刀霜的撒发十
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self.seed = 10 # 随机种子，置0则不设置随机种子
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self.train_eps = 400 # 训练的回合数
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self.test_eps = 30 # 测试的回合数
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################################################################################
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################################## 算法超参数 ###################################
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self.gamma = 0.90 # 强化学习中的折扣因子
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self.epsilon_start = 0.95 # e-greedy策略中初始epsilon
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self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
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self.epsilon_decay = 300 # e-greedy策略中epsilon的衰减率
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self.lr = 0.1 # 学习率
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################################################################################
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################################# 保存结果相关参数 ################################
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self.result_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/results/' # 保存结果的路径
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self.model_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/models/' # 保存模型的路径
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self.save = True # 是否保存图片
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################################################################################
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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 = [] # 记录奖励
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ma_rewards = [] # 记录滑动平均奖励
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for i_ep in range(cfg.train_eps):
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ep_reward = 0 # 记录每个回合的奖励
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state = env.reset() # 重置环境,即开始新的回合
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while True:
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action = agent.choose_action(state) # 根据算法选择一个动作
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next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互
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agent.update(state, action, reward, next_state, done) # Q学习算法更新
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state = next_state # 更新状态
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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("回合数：{}/{}，奖励{:.1f}".format(i_ep+1, cfg.train_eps,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 = [] # 滑动平均的奖励
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for i_ep in range(cfg.test_eps):
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ep_reward = 0 # 记录每个episode的reward
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state = env.reset() # 重置环境, 重新开一局（即开始新的一个回合）
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while True:
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action = agent.predict(state) # 根据算法选择一个动作
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next_state, reward, done, _ = env.step(action) # 与环境进行一个交互
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state = next_state # 更新状态
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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"回合数：{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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def env_agent_config(cfg,seed=1):
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'''创建环境和智能体
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Args:
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cfg ([type]): [description]
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seed (int, optional): 随机种子. Defaults to 1.
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Returns:
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env [type]: 环境
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agent : 智能体
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'''
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env = gym.make(cfg.env_name)
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env = CliffWalkingWapper(env)
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env.seed(seed) # 设置随机种子
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n_states = env.observation_space.n # 状态维度
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n_actions = env.action_space.n # 动作维度
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agent = QLearning(n_states,n_actions,cfg)
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return env,agent
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if __name__ == "__main__":
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cfg = Config()
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# 训练
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env, agent = env_agent_config(cfg, seed=1)
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rewards, ma_rewards = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path) # 创建保存结果和模型路径的文件夹
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agent.save(path=cfg.model_path) # 保存模型
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save_results(rewards, ma_rewards, tag='train',
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path=cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果
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# 测试
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env, agent = env_agent_config(cfg, seed=10)
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agent.load(path=cfg.model_path) # 导入模型
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rewards, ma_rewards = test(cfg, env, agent)
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save_results(rewards, ma_rewards, tag='test', path=cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, cfg, tag="test") # 画出结果
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