94 lines
3.4 KiB
Python
94 lines
3.4 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: 2021-12-22 11:13:23
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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 envs.gridworld_env import CliffWalkingWapper
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from QLearning.agent import QLearning
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from QLearning.train import train,test
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from common.utils 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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algo_name = 'Q-learning' # 算法名称
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env_name = 'CliffWalking-v0' # 环境名称
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
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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_name = algo_name # 算法名称
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self.env_name = env_name # 环境名称
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self.device = device # 检测GPU
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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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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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class PlotConfig:
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''' 绘图相关参数设置
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'''
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def __init__(self) -> None:
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self.algo_name = algo_name # 算法名称
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self.env_name = env_name # 环境名称
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self.device = device # 检测GPU
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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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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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state_dim = env.observation_space.n # 状态维度
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action_dim = env.action_space.n # 动作维度
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agent = QLearning(state_dim,action_dim,cfg)
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return env,agent
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cfg = QlearningConfig()
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plot_cfg = PlotConfig()
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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(plot_cfg.result_path, plot_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=plot_cfg.model_path) # 保存模型
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save_results(rewards, ma_rewards, tag='train',
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path=plot_cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, plot_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=plot_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=plot_cfg.result_path) # 保存结果
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plot_rewards(rewards, ma_rewards, plot_cfg, tag="test") # 画出结果
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