add DQN

codes/DQN/README.md

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+## 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)
+

codes/DQN/agent.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:50:49
+@LastEditor: John
+LastEditTime: 2021-03-13 14:56:23
+@Discription: 
+@Environment: python 3.7.7
+'''
+'''off-policy
+'''
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import random
+import math
+import numpy as np
+from common.memory import ReplayBuffer
+from common.model import MLP2
+class DQN:
+    def __init__(self, n_states, n_actions, cfg):
+
+        self.n_actions = n_actions  # 总的动作个数
+        self.device = cfg.device  # 设备，cpu或gpu等
+        self.gamma = cfg.gamma # 奖励的折扣因子
+        # e-greedy策略相关参数
+        self.sample_count = 0 # 用于epsilon的衰减计数
+        self.epsilon = 0
+        self.epsilon_start = cfg.epsilon_start
+        self.epsilon_end = cfg.epsilon_end
+        self.epsilon_decay = cfg.epsilon_decay
+        self.batch_size = cfg.batch_size
+        self.policy_net = MLP2(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
+        self.target_net = MLP2(n_states, n_actions,hidden_dim=cfg.hidden_dim).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=cfg.lr)
+        self.loss = 0
+        self.memory = ReplayBuffer(cfg.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.sample_count / self.epsilon_decay)
+            self.sample_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(self,path):
+        torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth')
+
+    def load(self,path):
+        self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))  

codes/DQN/main.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:48:57
+@LastEditor: John
+LastEditTime: 2021-03-13 14:56:50
+@Discription: 
+@Environment: python 3.7.7
+'''
+import sys,os
+sys.path.append(os.getcwd()) # 添加当前终端路径
+import gym
+import torch
+import datetime
+from DQN.agent import DQN
+from common.plot import plot_rewards
+from common.utils import save_results
+
+SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
+SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
+if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
+    os.mkdir(SAVED_MODEL_PATH)
+RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
+if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
+    os.mkdir(RESULT_PATH)
+
+class DQNConfig:
+    def __init__(self):
+        self.algo = "DQN" # 算法名称
+        self.gamma = 0.99
+        self.epsilon_start = 0.95 # e-greedy策略的初始epsilon
+        self.epsilon_end = 0.01
+        self.epsilon_decay = 200
+        self.lr = 0.01 # 学习率
+        self.memory_capacity = 800 # Replay Memory容量
+        self.batch_size = 64
+        self.train_eps = 250 # 训练的episode数目
+        self.train_steps = 200 # 训练每个episode的最大长度
+        self.target_update = 2 # target net的更新频率
+        self.eval_eps = 20 # 测试的episode数目
+        self.eval_steps = 200 # 测试每个episode的最大长度
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
+        self.hidden_dim = 128 # 神经网络隐藏层维度
+
+def train(cfg,env,agent):
+    print('Start to train !')
+    rewards = []
+    ma_rewards = [] # 滑动平均的reward
+    ep_steps = []
+    for i_episode in range(cfg.train_eps):
+        state = env.reset() # reset环境状态
+        ep_reward = 0
+        for i_step in range(cfg.train_steps):
+            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:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step,done))
+        ep_steps.append(i_step)
+        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('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_actions,cfg)
+    rewards,ma_rewards = train(cfg,env,agent)
+    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)