update codes

codes/DQN/task0_train.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-12 00:48:57
-@LastEditor: John
-LastEditTime: 2021-09-15 15:34:13
-@Discription: 
-@Environment: python 3.7.7
-'''
-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) # 添加父路径到系统路径sys.path
-
-import gym
-import torch
-import datetime
-
-from common.utils import save_results, make_dir
-from common.plot import plot_rewards,plot_rewards_cn
-from DQN.agent import DQN
-
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-
-class DQNConfig:
-    def __init__(self):
-        self.algo = "DQN"  # 算法名称
-        self.env = 'CartPole-v0' # 环境名称
-        self.result_path = curr_path+"/outputs/" + self.env + \
-            '/'+curr_time+'/results/'  # 保存结果的路径
-        self.model_path = curr_path+"/outputs/" + self.env + \
-            '/'+curr_time+'/models/'  # 保存模型的路径
-        self.train_eps = 200 # 训练的回合数
-        self.eval_eps = 30 # 测试的回合数
-        self.gamma = 0.95 # 强化学习中的折扣因子
-        self.epsilon_start = 0.90 # 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 = 500 # e-greedy策略中epsilon的衰减率
-        self.lr = 0.0001  # 学习率
-        self.memory_capacity = 100000  # 经验回放的容量
-        self.batch_size = 64 # mini-batch SGD中的批量大小
-        self.target_update = 4 # 目标网络的更新频率
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
-        self.hidden_dim = 256  # hidden size of net
-        
-def env_agent_config(cfg,seed=1):
-    env = gym.make(cfg.env)  
-    env.seed(seed)
-    n_states = env.observation_space.shape[0]
-    n_actions = env.action_space.n
-    agent = DQN(n_states,n_actions,cfg)
-    return env,agent
-    
-def train(cfg, env, agent):
-    print('开始训练!')
-    print(f'环境：{cfg.env}, 算法：{cfg.algo}, 设备：{cfg.device}')
-    rewards = [] # 记录奖励
-    ma_rewards = []  # 记录滑动平均奖励
-    for i_ep in range(cfg.train_eps):
-        state = env.reset()
-        done = False
-        ep_reward = 0
-        while True:
-            action = agent.choose_action(state)
-            next_state, reward, done, _ = env.step(action)
-            ep_reward += reward
-            agent.memory.push(state, action, reward, next_state, done)
-            state = next_state
-            agent.update()
-            if done:
-                break
-        if (i_ep+1) % cfg.target_update == 0:
-            agent.target_net.load_state_dict(agent.policy_net.state_dict())
-        if (i_ep+1)%10 == 0:
-            print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, ep_reward))
-        rewards.append(ep_reward)
-        # save ma_rewards
-        if ma_rewards:
-            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
-        else:
-            ma_rewards.append(ep_reward)
-    print('完成训练！')
-    return rewards, ma_rewards
-
-def eval(cfg,env,agent):
-    print('开始测试!')
-    print(f'环境：{cfg.env}, 算法：{cfg.algo}, 设备：{cfg.device}')
-    rewards = []  
-    ma_rewards = [] # moving average rewards
-    for i_ep in range(cfg.eval_eps):
-        ep_reward = 0  # reward per episode
-        state = env.reset()  
-        while True:
-            action = agent.predict(state) 
-            next_state, reward, done, _ = env.step(action)  
-            state = next_state  
-            ep_reward += reward
-            if done:
-                break
-        rewards.append(ep_reward)
-        if ma_rewards:
-            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
-        else:
-            ma_rewards.append(ep_reward)
-        print(f"回合：{i_ep+1}/{cfg.eval_eps}, 奖励：{ep_reward:.1f}")
-    print('完成测试！')
-    return rewards,ma_rewards
-
-if __name__ == "__main__":
-    cfg = DQNConfig()
-    # 训练
-    env,agent = env_agent_config(cfg,seed=1)
-    rewards, ma_rewards = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)
-    agent.save(path=cfg.model_path)
-    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
-    plot_rewards_cn(rewards, ma_rewards, tag="train",
-                 algo=cfg.algo, path=cfg.result_path)
-    # 测试
-    env,agent = env_agent_config(cfg,seed=10)
-    agent.load(path=cfg.model_path)
-    rewards,ma_rewards = eval(cfg,env,agent)
-    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
-    plot_rewards_cn(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)