diff --git a/codes/dqn/README.md b/codes/dqn/README.md
deleted file mode 100644
index a8e141b..0000000
--- a/codes/dqn/README.md
+++ /dev/null
@@ -1,35 +0,0 @@
-## 思路
-
-见[我的博客](https://blog.csdn.net/JohnJim0/article/details/109557173)
-## 环境
-
-python 3.7.9
-
-pytorch 1.6.0
-
-tensorboard 2.3.0 
-
-torchvision 0.7.0 
-
-## 使用
-
-train: 
-
-```python
-python main.py 
-```
-
-eval: 
-
-```python
-python main.py --train 0 
-```
-可视化：
-```python
-tensorboard --logdir logs 
-```
-
-## Torch知识
-
-[with torch.no_grad()](https://www.jianshu.com/p/1cea017f5d11)
-
diff --git a/codes/dqn/agent.py b/codes/dqn/agent.py
deleted file mode 100644
index 1ad82df..0000000
--- a/codes/dqn/agent.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-12 00:50:49
-@LastEditor: John
-LastEditTime: 2020-11-22 11:12:30
-@Discription: 
-@Environment: python 3.7.7
-'''
-'''off-policy
-'''
-
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torch.nn.functional as F
-import random
-import math
-import numpy as np
-from memory import ReplayBuffer
-from model import FCN
-class DQN:
-    def __init__(self, n_states, n_actions, gamma=0.99, epsilon_start=0.9, epsilon_end=0.05, epsilon_decay=200, memory_capacity=10000, policy_lr=0.01, batch_size=128, device="cpu"):
-        
-        self.n_actions = n_actions  # 总的动作个数
-        self.device = device  # 设备，cpu或gpu等
-        self.gamma = gamma # 奖励的折扣因子
-        # e-greedy策略相关参数
-        self.actions_count = 0 # 用于epsilon的衰减计数
-        self.epsilon = 0
-        self.epsilon_start = epsilon_start
-        self.epsilon_end = epsilon_end
-        self.epsilon_decay = epsilon_decay
-        self.batch_size = batch_size
-        self.policy_net = FCN(n_states, n_actions).to(self.device)
-        self.target_net = FCN(n_states, n_actions).to(self.device)
-        # target_net的初始模型参数完全复制policy_net
-        self.target_net.load_state_dict(self.policy_net.state_dict())
-        self.target_net.eval()  # 不启用 BatchNormalization 和 Dropout
-        # 可查parameters()与state_dict()的区别，前者require_grad=True
-        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=policy_lr)
-        self.loss = 0
-        self.memory = ReplayBuffer(memory_capacity)
-
-    def choose_action(self, state, train=True):
-        '''选择动作
-        '''
-        if train:
-            self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
-                math.exp(-1. * self.actions_count / self.epsilon_decay)
-            self.actions_count += 1
-            if random.random() > self.epsilon:
-                with torch.no_grad():
-                    # 先转为张量便于丢给神经网络,state元素数据原本为float64
-                    # 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
-                    state = torch.tensor(
-                        [state], device=self.device, dtype=torch.float32)
-                    # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
-                    q_value = self.policy_net(state)
-                    # tensor.max(1)返回每行的最大值以及对应的下标，
-                    # 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
-                    # 所以tensor.max(1)[1]返回最大值对应的下标，即action
-                    action = q_value.max(1)[1].item()  
-            else:
-                action = random.randrange(self.n_actions)
-            return action
-        else: 
-            with torch.no_grad(): # 取消保存梯度
-                    # 先转为张量便于丢给神经网络,state元素数据原本为float64
-                    # 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
-                    state = torch.tensor(
-                        [state], device='cpu', dtype=torch.float32) # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
-                    q_value = self.target_net(state)
-                    # tensor.max(1)返回每行的最大值以及对应的下标，
-                    # 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
-                    # 所以tensor.max(1)[1]返回最大值对应的下标，即action
-                    action = q_value.max(1)[1].item() 
-            return action
-    def update(self):
-
-        if len(self.memory) < self.batch_size:
-            return
-        # 从memory中随机采样transition
-        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
-            self.batch_size)
-        '''转为张量
-        例如tensor([[-4.5543e-02, -2.3910e-01,  1.8344e-02,  2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02,  2.3400e-01]])'''
-        state_batch = torch.tensor(
-            state_batch, device=self.device, dtype=torch.float)
-        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
-            1)  # 例如tensor([[1],...,[0]])
-        reward_batch = torch.tensor(
-            reward_batch, device=self.device, dtype=torch.float)  # tensor([1., 1.,...,1])
-        next_state_batch = torch.tensor(
-            next_state_batch, device=self.device, dtype=torch.float)
-        done_batch = torch.tensor(np.float32(
-            done_batch), device=self.device).unsqueeze(1)  # 将bool转为float然后转为张量
-
-        '''计算当前(s_t,a)对应的Q(s_t, a)'''
-        '''torch.gather:对于a=torch.Tensor([[1,2],[3,4]]),那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])'''
-        q_values = self.policy_net(state_batch).gather(
-            dim=1, index=action_batch)  # 等价于self.forward
-        # 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
-        next_state_values = self.target_net(
-            next_state_batch).max(1)[0].detach()  # 比如tensor([ 0.0060, -0.0171,...,])
-        # 计算 expected_q_value
-        # 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
-        expected_q_values = reward_batch + self.gamma * \
-            next_state_values * (1-done_batch[0])
-        # self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
-        self.loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))  # 计算 均方误差loss
-        # 优化模型
-        self.optimizer.zero_grad()  # zero_grad清除上一步所有旧的gradients from the last step
-        # loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
-        self.loss.backward()
-        for param in self.policy_net.parameters():  # clip防止梯度爆炸
-            param.grad.data.clamp_(-1, 1)
-            
-        self.optimizer.step()  # 更新模型
-
-    def save_model(self,path):
-        torch.save(self.target_net.state_dict(), path)
-
-    def load_model(self,path):
-        self.target_net.load_state_dict(torch.load(path))  
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diff --git a/codes/dqn/main.py b/codes/dqn/main.py
deleted file mode 100644
index b024320..0000000
--- a/codes/dqn/main.py
+++ /dev/null
@@ -1,153 +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: 2021-01-05 09:41:02
-@Discription: 
-@Environment: python 3.7.7
-'''
-import gym
-import torch
-from agent import DQN
-import argparse
-from torch.utils.tensorboard import SummaryWriter
-import datetime
-import os
-from utils import save_results,save_model
-
-SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
-SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
-RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/'
-
-def get_args():
-    '''模型参数
-    '''
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--train", default=1, type=int)  # 1 表示训练，0表示只进行eval
-    parser.add_argument("--gamma", default=0.99,
-                        type=float)  # q-learning中的gamma
-    parser.add_argument("--epsilon_start", default=0.95,
-                        type=float)  # 基于贪心选择action对应的参数epsilon
-    parser.add_argument("--epsilon_end", default=0.01, type=float)
-    parser.add_argument("--epsilon_decay", default=500, type=float)
-    parser.add_argument("--policy_lr", default=0.01, type=float)
-    parser.add_argument("--memory_capacity", default=1000,
-                        type=int, help="capacity of Replay Memory") 
-
-    parser.add_argument("--batch_size", default=32, type=int,
-                        help="batch size of memory sampling")
-    parser.add_argument("--train_eps", default=200, type=int) # 训练的最大episode数目
-    parser.add_argument("--train_steps", default=200, type=int)
-    parser.add_argument("--target_update", default=2, type=int,
-                        help="when(every default 2 eisodes) to update target net ") # 更新频率
-
-    parser.add_argument("--eval_eps", default=100, type=int)  # 训练的最大episode数目
-    parser.add_argument("--eval_steps", default=200,
-                        type=int)  # 训练每个episode的长度
-    config = parser.parse_args()
-
-    return config
-def train(cfg):
-    print('Start to train ! \n')
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
-    env = gym.make('CartPole-v0')
-    env.seed(1) # 设置env随机种子
-    n_states = env.observation_space.shape[0]
-    n_actions = env.action_space.n
-    agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
-                epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
-    rewards = []
-    moving_average_rewards = []
-    ep_steps = []
-    log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
-    writer = SummaryWriter(log_dir)
-    for i_episode in range(1, cfg.train_eps+1):
-        state = env.reset() # reset环境状态
-        ep_reward = 0
-        for i_step in range(1, cfg.train_steps+1):
-            action = agent.choose_action(state) # 根据当前环境state选择action
-            next_state, reward, done, _ = env.step(action) # 更新环境参数
-            ep_reward += reward
-            agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
-            state = next_state # 跳转到下一个状态
-            agent.update() # 每步更新网络
-            if done:
-                break
-        # 更新target network，复制DQN中的所有weights and biases
-        if i_episode % cfg.target_update == 0:
-            agent.target_net.load_state_dict(agent.policy_net.state_dict())
-        print('Episode:', i_episode, ' Reward: %i' %
-              int(ep_reward), 'n_steps:', i_step, 'done: ', done,' Explore: %.2f' % agent.epsilon)
-        ep_steps.append(i_step)
-        rewards.append(ep_reward)
-        # 计算滑动窗口的reward
-        if i_episode == 1:
-            moving_average_rewards.append(ep_reward)
-        else:
-            moving_average_rewards.append(
-                0.9*moving_average_rewards[-1]+0.1*ep_reward)
-        writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
-        writer.add_scalar('steps_of_each_episode',
-                          ep_steps[-1], i_episode)
-    writer.close()
-    print('Complete training！')
-    ''' 保存模型 '''
-    save_model(agent,model_path=SAVED_MODEL_PATH)
-    '''存储reward等相关结果'''
-    save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
-    
-
-def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
-    print('start to eval ! \n')
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
-    env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
-    env.seed(1) # 设置env随机种子
-    n_states = env.observation_space.shape[0]
-    n_actions = env.action_space.n
-    agent = DQN(n_states=n_states, n_actions=n_actions, device="cpu", gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
-                epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
-    agent.load_model(saved_model_path+'checkpoint.pth')
-    rewards = []
-    moving_average_rewards = []
-    ep_steps = []
-    log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE
-    writer = SummaryWriter(log_dir)
-    for i_episode in range(1, cfg.eval_eps+1):
-        state = env.reset()  # reset环境状态
-        ep_reward = 0
-        for i_step in range(1, cfg.eval_steps+1):
-            action = agent.choose_action(state,train=False)  # 根据当前环境state选择action
-            next_state, reward, done, _ = env.step(action)  # 更新环境参数
-            ep_reward += reward
-            state = next_state  # 跳转到下一个状态
-            if done:
-                break
-        print('Episode:', i_episode, ' Reward: %i' %
-              int(ep_reward), 'n_steps:', i_step, 'done: ', done)
-        ep_steps.append(i_step)
-        rewards.append(ep_reward)
-        # 计算滑动窗口的reward
-        if i_episode == 1:
-            moving_average_rewards.append(ep_reward)
-        else:
-            moving_average_rewards.append(
-                0.9*moving_average_rewards[-1]+0.1*ep_reward)
-        writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
-        writer.add_scalar('steps_of_each_episode',
-                          ep_steps[-1], i_episode)
-    writer.close()
-    '''存储reward等相关结果'''
-    save_results(rewards,moving_average_rewards,ep_steps,tag='eval',result_path=RESULT_PATH)
-    print('Complete evaling！')
-    
-if __name__ == "__main__":
-    cfg = get_args()
-    if cfg.train:
-        train(cfg)
-        eval(cfg)
-    else:
-        model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"
-        eval(cfg,saved_model_path=model_path)
diff --git a/codes/dqn/memory.py b/codes/dqn/memory.py
deleted file mode 100644
index 657bfc0..0000000
--- a/codes/dqn/memory.py
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-10 15:27:16
-@LastEditor: John
-@LastEditTime: 2020-06-14 11:36:24
-@Discription: 
-@Environment: python 3.7.7
-'''
-import random
-import numpy as np
-
-class ReplayBuffer:
-    
-    def __init__(self, capacity):
-        self.capacity = capacity
-        self.buffer = []
-        self.position = 0
-    
-    def push(self, state, action, reward, next_state, done):
-        if len(self.buffer) < self.capacity:
-            self.buffer.append(None)
-        self.buffer[self.position] = (state, action, reward, next_state, done)
-        self.position = (self.position + 1) % self.capacity
-    
-    def sample(self, batch_size):
-        batch = random.sample(self.buffer, batch_size)
-        state, action, reward, next_state, done =  zip(*batch)
-        return state, action, reward, next_state, done
-    
-    def __len__(self):
-        return len(self.buffer)
-
diff --git a/codes/dqn/model.py b/codes/dqn/model.py
deleted file mode 100644
index a4642d8..0000000
--- a/codes/dqn/model.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-12 00:47:02
-@LastEditor: John
-LastEditTime: 2020-08-19 16:55:54
-@Discription: 
-@Environment: python 3.7.7
-'''
-import torch.nn as nn
-import torch.nn.functional as F
-
-class FCN(nn.Module):
-    def __init__(self, n_states=4, n_actions=18):
-        """ 初始化q网络，为全连接网络
-            n_states: 输入的feature即环境的state数目
-            n_actions: 输出的action总个数
-        """
-        super(FCN, self).__init__()
-        self.fc1 = nn.Linear(n_states, 128) # 输入层
-        self.fc2 = nn.Linear(128, 128) # 隐藏层
-        self.fc3 = nn.Linear(128, n_actions) # 输出层
-        
-    def forward(self, x):
-        # 各层对应的激活函数
-        x = F.relu(self.fc1(x)) 
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
\ No newline at end of file
diff --git a/codes/dqn/plot.py b/codes/dqn/plot.py
deleted file mode 100644
index 59680c2..0000000
--- a/codes/dqn/plot.py
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-@Author: John
-@Email: johnjim0816@gmail.com
-@Date: 2020-06-11 16:30:09
-@LastEditor: John
-LastEditTime: 2020-11-23 13:48:31
-@Discription: 
-@Environment: python 3.7.7
-'''
-import matplotlib.pyplot as plt
-import seaborn as sns
-import numpy as np
-import os 
-
-def plot(item,ylabel='rewards_train', save_fig = True):
-    '''plot using searborn to plot 
-    '''
-    sns.set()
-    plt.figure()
-    plt.plot(np.arange(len(item)), item)
-    plt.title(ylabel+' of DQN') 
-    plt.ylabel(ylabel)
-    plt.xlabel('episodes')
-    if save_fig:
-        plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
-    plt.show()
-
-if __name__ == "__main__":
-
-    output_path = os.path.split(os.path.abspath(__file__))[0]+"/result/"
-    tag = 'train'
-    rewards=np.load(output_path+"rewards_"+tag+".npy", )
-    moving_average_rewards=np.load(output_path+"moving_average_rewards_"+tag+".npy",)
-    steps=np.load(output_path+"steps_"+tag+".npy")
-    plot(rewards)
-    plot(moving_average_rewards,ylabel='moving_average_rewards_'+tag)
-    plot(steps,ylabel='steps_'+tag)
-    tag = 'eval'
-    rewards=np.load(output_path+"rewards_"+tag+".npy", )
-    moving_average_rewards=np.load(output_path+"moving_average_rewards_"+tag+".npy",)
-    steps=np.load(output_path+"steps_"+tag+".npy")
-    plot(rewards,ylabel='rewards_'+tag)
-    plot(moving_average_rewards,ylabel='moving_average_rewards_'+tag)
-    plot(steps,ylabel='steps_'+tag) 
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diff --git a/codes/dqn/utils.py b/codes/dqn/utils.py
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--- a/codes/dqn/utils.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-10-15 21:28:00
-LastEditor: John
-LastEditTime: 2020-10-30 16:56:55
-Discription: 
-Environment: 
-'''
-import os
-import numpy as np
-
-
-def save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path='./result'):
-    '''保存reward等结果
-    '''
-    if not os.path.exists(result_path): # 检测是否存在文件夹
-        os.mkdir(result_path)
-    np.save(result_path+'rewards_'+tag+'.npy', rewards)
-    np.save(result_path+'moving_average_rewards_'+tag+'.npy', moving_average_rewards)
-    np.save(result_path+'steps_'+tag+'.npy',ep_steps )
-    print('results saved!')
-
-def save_model(agent,model_path='./saved_model'):
-    if not os.path.exists(model_path): # 检测是否存在文件夹
-        os.mkdir(model_path)
-    agent.save_model(model_path+'checkpoint.pth')
-    print('model saved！')
\ No newline at end of file