update

codes/QLearning/task0.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-09-11 23:03:00
+LastEditor: John
+LastEditTime: 2021-12-22 11:13:23
+Discription: 
+Environment: 
+'''
+import sys
+import 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
+
+import gym
+import torch
+import datetime
+
+from envs.gridworld_env import CliffWalkingWapper
+from QLearning.agent import QLearning
+from QLearning.train import train,test
+from common.utils import plot_rewards,plot_rewards_cn
+from common.utils import save_results,make_dir
+
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
+algo_name = 'Q-learning'  # 算法名称
+env_name = 'CliffWalking-v0'  # 环境名称
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
+class QlearningConfig:
+    '''训练相关参数'''
+    def __init__(self):
+        self.algo_name = algo_name # 算法名称
+        self.env_name = env_name # 环境名称
+        self.device = device # 检测GPU
+        self.train_eps = 400 # 训练的回合数
+        self.test_eps = 30 # 测试的回合数
+        self.gamma = 0.9 # reward的衰减率
+        self.epsilon_start = 0.95 # e-greedy策略中初始epsilon
+        self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
+        self.epsilon_decay = 300 # e-greedy策略中epsilon的衰减率
+        self.lr = 0.1 # 学习率      
+class PlotConfig:
+    ''' 绘图相关参数设置
+    '''
+
+    def __init__(self) -> None:
+        self.algo_name = algo_name  # 算法名称
+        self.env_name = env_name  # 环境名称
+        self.device = device # 检测GPU
+        self.result_path = curr_path + "/outputs/" + self.env_name + \
+            '/' + curr_time + '/results/'  # 保存结果的路径
+        self.model_path = curr_path + "/outputs/" + self.env_name + \
+            '/' + curr_time + '/models/'  # 保存模型的路径
+        self.save = True  # 是否保存图片
+        
+def env_agent_config(cfg,seed=1):
+    '''创建环境和智能体
+    Args:
+        cfg ([type]): [description]
+        seed (int, optional): 随机种子. Defaults to 1.
+    Returns:
+        env [type]: 环境
+        agent : 智能体
+    '''    
+    env = gym.make(cfg.env_name)  
+    env = CliffWalkingWapper(env)
+    env.seed(seed) # 设置随机种子
+    n_states = env.observation_space.n # 状态维度
+    n_actions = env.action_space.n # 动作维度
+    agent = QLearning(n_states,n_actions,cfg)
+    return env,agent
+
+cfg = QlearningConfig()
+plot_cfg = PlotConfig()
+# 训练
+env, agent = env_agent_config(cfg, seed=1)
+rewards, ma_rewards = train(cfg, env, agent)
+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
+agent.save(path=plot_cfg.model_path)  # 保存模型
+save_results(rewards, ma_rewards, tag='train',
+            path=plot_cfg.result_path)  # 保存结果
+plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")  # 画出结果
+# 测试
+env, agent = env_agent_config(cfg, seed=10)
+agent.load(path=plot_cfg.model_path)  # 导入模型
+rewards, ma_rewards = test(cfg, env, agent)
+save_results(rewards, ma_rewards, tag='test', path=plot_cfg.result_path)  # 保存结果
+plot_rewards(rewards, ma_rewards, plot_cfg, tag="test")  # 画出结果
+        
+