hot update DQN

projects/codes/QLearning/outputs/CliffWalking-v0/20220802-163256/results/params.json

@@ -1,15 +0,0 @@
-{
-    "algo_name": "Q-learning",
-    "env_name": "CliffWalking-v0",
-    "train_eps": 400,
-    "test_eps": 20,
-    "gamma": 0.9,
-    "epsilon_start": 0.95,
-    "epsilon_end": 0.01,
-    "epsilon_decay": 300,
-    "lr": 0.1,
-    "device": "cpu",
-    "result_path": "/root/Desktop/rl-tutorials/codes/QLearning/outputs/CliffWalking-v0/20220802-163256/results/",
-    "model_path": "/root/Desktop/rl-tutorials/codes/QLearning/outputs/CliffWalking-v0/20220802-163256/models/",
-    "save_fig": true
-}

projects/codes/QLearning/task0.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-09-11 23:03:00
-LastEditor: John
-LastEditTime: 2022-08-10 11:25:56
-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
-import argparse
-from envs.gridworld_env import CliffWalkingWapper
-from qlearning import QLearning
-from common.utils import plot_rewards,save_args
-from common.utils import save_results,make_dir
-
-def get_args():
-    """ 
-    """
-    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-    parser = argparse.ArgumentParser(description="hyperparameters")      
-    parser.add_argument('--algo_name',default='Q-learning',type=str,help="name of algorithm")
-    parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment")
-    parser.add_argument('--train_eps',default=400,type=int,help="episodes of training") # 训练的回合数
-    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") # 测试的回合数
-    parser.add_argument('--gamma',default=0.90,type=float,help="discounted factor") # 折扣因子
-    parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") #  e-greedy策略中初始epsilon
-    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") # e-greedy策略中的终止epsilon
-    parser.add_argument('--epsilon_decay',default=300,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率
-    parser.add_argument('--lr',default=0.1,type=float,help="learning rate")
-    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
-    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/results/',type=str )
-    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/models/',type=str,help="path to save models")
-    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
-    args = parser.parse_args()                          
-    return args
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
-      
-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 = []  # 记录奖励
-    for i_ep in range(cfg.train_eps):
-        ep_reward = 0  # 记录每个回合的奖励
-        state = env.reset()  # 重置环境,即开始新的回合
-        while True:
-            action = agent.sample(state)  # 根据算法采样一个动作
-            next_state, reward, done, _ = env.step(action)  # 与环境进行一次动作交互
-            agent.update(state, action, reward, next_state, done)  # Q学习算法更新
-            state = next_state  # 更新状态
-            ep_reward += reward
-            if done:
-                break
-        rewards.append(ep_reward)
-        print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}")
-    print('完成训练！')
-    return {"rewards":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}')
-    rewards = []  # 记录所有回合的奖励
-    for i_ep in range(cfg.test_eps):
-        ep_reward = 0  # 记录每个episode的reward
-        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个回合）
-        while True:
-            action = agent.predict(state)  # 根据算法选择一个动作
-            next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互
-            state = next_state  # 更新状态
-            ep_reward += reward
-            if done:
-                break
-        rewards.append(ep_reward)
-        print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}")
-    print('完成测试！')
-    return {"rewards":rewards}
-        
-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
-    print(f"状态数：{n_states}，动作数：{n_actions}")
-    agent = QLearning(n_actions,cfg)
-    return env,agent
-if __name__ == "__main__":
-    cfg = get_args()
-    # 训练
-    env, agent = env_agent_config(cfg)
-    res_dic = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)  
-    save_args(cfg) # save parameters
-    agent.save(path=cfg.model_path)  # save model
-    save_results(res_dic, tag='train',
-                 path=cfg.result_path)  
-    plot_rewards(res_dic['rewards'], cfg, tag="train")  
-    # 测试
-    env, agent = env_agent_config(cfg)
-    agent.load(path=cfg.model_path)  # 导入模型
-    res_dic = test(cfg, env, agent)
-    save_results(res_dic, tag='test',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(res_dic['rewards'], cfg, tag="test")  # 画出结果
-
-        
-