hot update PG

projects/codes/Sarsa/task0.py

@@ -1,118 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2021-03-11 17:59:16
-LastEditor: John
-LastEditTime: 2022-08-04 22:28:51
-Discription: 
-Environment: 
-'''
-import sys,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 datetime
-import argparse
-from envs.racetrack_env import RacetrackEnv
-from Sarsa.sarsa import Sarsa
-from common.utils import save_results,make_dir,plot_rewards,save_args
-
-def get_args():
-    """ 超参数
-    """
-    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='Sarsa',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=300,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('--ep_max_steps',default=200,type=int) # 每回合最大的部署
-    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor") # 折扣因子
-    parser.add_argument('--epsilon_start',default=0.90,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=200,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率
-    parser.add_argument('--lr',default=0.2,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/' )
-    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
-            '/' + curr_time + '/models/' ) # 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
-
-
-def env_agent_config(cfg,seed=1):
-    env = RacetrackEnv()
-    n_actions = 9 # 动作数
-    agent = Sarsa(n_actions,cfg)
-    return env,agent
-        
-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):
-        state = env.reset()
-        action = agent.sample(state)
-        ep_reward = 0
-        # while True:
-        for _ in range(cfg.ep_max_steps):
-            next_state, reward, done = env.step(action)
-            ep_reward+=reward
-            next_action = agent.sample(next_state)
-            agent.update(state, action, reward, next_state, next_action,done)
-            state = next_state
-            action = next_action
-            if done:
-                break  
-        rewards.append(ep_reward)
-        if (i_ep+1)%2==0:
-            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 = []
-    for i_ep in range(cfg.test_eps):
-        state = env.reset()
-        ep_reward = 0
-        # while True:
-        for _ in range(cfg.ep_max_steps):
-            action = agent.predict(state)
-            next_state, reward, done = env.step(action)
-            ep_reward+=reward
-            state = next_state
-            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}
-        
-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
-    
-    
-