127 lines
5.0 KiB
Python
127 lines
5.0 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: 2021-09-23 12:22:58
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Discription:
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Environment:
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'''
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import sys,os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前路径
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parent_path=os.path.dirname(curr_path) # 父路径，这里就是我们的项目路径
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sys.path.append(parent_path) # 由于需要引用项目路径下的其他模块比如envs，所以需要添加路径到sys.path
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import gym
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import torch
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import datetime
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from envs.gridworld_env import CliffWalkingWapper
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from QLearning.agent import QLearning
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from common.plot import plot_rewards,plot_rewards_cn
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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 QlearningConfig:
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'''训练相关参数'''
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def __init__(self):
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self.algo = 'Q-learning' # 算法名称
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self.env = 'CliffWalking-v0' # 环境名称
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self.result_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/results/' # 保存结果的路径
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self.model_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/models/' # 保存模型的路径
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self.train_eps = 400 # 训练的回合数
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self.eval_eps = 30 # 测试的回合数
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self.gamma = 0.9 # reward的衰减率
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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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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
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def env_agent_config(cfg,seed=1):
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env = gym.make(cfg.env)
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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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def train(cfg,env,agent):
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print('开始训练！')
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print(f'环境:{cfg.env}, 算法:{cfg.algo}, 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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print(reward)
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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 eval(cfg,env,agent):
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print('开始测试！')
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print(f'环境：{cfg.env}, 算法：{cfg.algo}, 设备：{cfg.device}')
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for item in agent.Q_table.items():
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print(item)
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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.eval_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.eval_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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if __name__ == "__main__":
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cfg = QlearningConfig()
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# 训练
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env,agent = env_agent_config(cfg,seed=0)
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rewards,ma_rewards = train(cfg,env,agent)
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make_dir(cfg.result_path,cfg.model_path) # 创建文件夹
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agent.save(path=cfg.model_path) # 保存模型
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for item in agent.Q_table.items():
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print(item)
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save_results(rewards,ma_rewards,tag='train',path=cfg.result_path) # 保存结果
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plot_rewards_cn(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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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) # 加载模型
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rewards,ma_rewards = eval(cfg,env,agent)
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save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
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plot_rewards_cn(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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