update

codes/DQN/task1.py

@@ -1,88 +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: 2021-04-13 18:49:44
-@Discription: 
-@Environment: python 3.7.7
-'''
-import sys,os
-curr_path = os.path.dirname(__file__)
-parent_path=os.path.dirname(curr_path) 
-sys.path.append(parent_path) # add current terminal path to sys.path
-
-import gym
-import torch
-import datetime
-from DQN.agent import DQN
-from common.plot import plot_rewards
-from common.utils import save_results,make_dir,del_empty_dir
-
-SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
-SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
-RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
-make_dir(curr_path+"/saved_model/",curr_path+"/results/")
-del_empty_dir(curr_path+"/saved_model/",curr_path+"/results/")
-
-class DQNConfig:
-    def __init__(self):
-        self.env = 'LunarLander-v2'
-        self.algo = "DQN"  # name of algo
-        self.gamma = 0.95
-        self.epsilon_start = 1 # e-greedy策略的初始epsilon
-        self.epsilon_end = 0.01
-        self.epsilon_decay = 500
-        self.lr = 0.0001 # learning rate
-        self.memory_capacity = 1000000 # Replay Memory容量
-        self.batch_size = 64
-        self.train_eps = 300 # 训练的episode数目
-        self.train_steps = 1000
-        self.target_update = 2 # target net的更新频率
-        self.eval_eps = 20 # 测试的episode数目
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
-        self.hidden_dim = 256 # 神经网络隐藏层维度
- 
-def train(cfg,env,agent):
-    print('Start to train !')
-    rewards = []
-    ma_rewards = [] # moveing average reward
-    for i_episode in range(cfg.train_eps):
-        state = env.reset() 
-        ep_reward = 0
-        for i_step in range(cfg.train_steps):
-            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_episode % cfg.target_update == 0:
-            agent.target_net.load_state_dict(agent.policy_net.state_dict())
-        print('Episode:{}/{}, Reward:{}'.format(i_episode+1,cfg.train_eps,ep_reward))
-        rewards.append(ep_reward)
-        # 计算滑动窗口的reward
-        if ma_rewards:
-            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
-        else:
-            ma_rewards.append(ep_reward)   
-    print('Complete training！')
-    return rewards,ma_rewards
-
-if __name__ == "__main__":
-    cfg = DQNConfig()
-    env = gym.make(cfg.env)
-    env.seed(1)
-    state_dim = env.observation_space.shape[0]
-    action_dim = env.action_space.n
-    agent = DQN(state_dim,action_dim,cfg)
-    rewards,ma_rewards = train(cfg,env,agent)
-    make_dir(SAVED_MODEL_PATH,RESULT_PATH)
-    agent.save(path=SAVED_MODEL_PATH)
-    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
-    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
-    del_empty_dir(SAVED_MODEL_PATH,RESULT_PATH)