update DoubleDQN

codes/DoubleDQN/README.md

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+## 思路
+
+见[博客](https://blog.csdn.net/JohnJim0/article/details/111552545)
+
+## 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 
+```

codes/DoubleDQN/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: 2020-12-22 14:44:46
+@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.actions_count = 0
+        self.n_actions = n_actions  # 总的动作个数
+        self.device = device  # 设备，cpu或gpu等
+        self.gamma = gamma
+        # e-greedy策略相关参数
+        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():
+                    # 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)
+        q_values = self.policy_net(state_batch) 
+        next_q_values = self.policy_net(state_batch)
+        # 代入当前选择的action，得到Q(s_t|a=a_t)
+        q_value = q_values.gather(dim=1, index=action_batch)
+        '''以下是Nature DQN的q_target计算方式
+        # 计算所有next states的Q'(s_{t+1})的最大值，Q'为目标网络的q函数
+        next_q_state_value = self.target_net(
+            next_state_batch).max(1)[0].detach()  # 比如tensor([ 0.0060, -0.0171,...,])
+        # 计算 q_target
+        # 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
+        q_target = reward_batch + self.gamma * next_q_state_value * (1-done_batch[0])
+        '''
+        '''以下是Double DQNq_target计算方式，与NatureDQN稍有不同'''
+        next_target_values = self.target_net(
+            next_state_batch)
+        # 选出Q(s_t‘, a)对应的action，代入到next_target_values获得target net对应的next_q_value，即Q’(s_t|a=argmax Q(s_t‘, a))
+        next_target_q_value = next_target_values.gather(1, torch.max(next_q_values, 1)[1].unsqueeze(1)).squeeze(1)
+        q_target = reward_batch + self.gamma * next_target_q_value * (1-done_batch[0])
+        self.loss = nn.MSELoss()(q_value, q_target.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))  

codes/DoubleDQN/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: 2020-12-22 15:39:46
+@Discription: 
+@Environment: python 3.7.7
+'''
+import gym
+import torch
+from torch.utils.tensorboard import SummaryWriter
+import os
+from agent import DQN
+from params import SEQUENCE,SAVED_MODEL_PATH,RESULT_PATH
+from params import get_args
+from utils import save_results
+
+def train(cfg):
+    print('Start to train !')
+    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=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 '''
+    if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
+        os.mkdir(SAVED_MODEL_PATH)
+    agent.save_model(SAVED_MODEL_PATH+'checkpoint.pth')
+    print('model saved！')
+    '''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 !')
+    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=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)
+    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)

codes/DoubleDQN/memory.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-10 15:27:16
+@LastEditor: John
+LastEditTime: 2020-12-22 12:56:27
+@Discription: 
+@Environment: python 3.7.7
+'''
+import random
+import numpy as np
+
+class ReplayBuffer:
+    
+    def __init__(self, capacity):
+        self.capacity = capacity # buffer的最大容量
+        self.buffer = []
+        self.position = 0
+    
+    def push(self, state, action, reward, next_state, done):
+        '''以队列的方式将样本填入buffer中
+        '''
+        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):
+        '''随机采样batch_size个样本
+        '''
+        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):
+        '''返回buffer的长度
+        '''
+        return len(self.buffer)
+

codes/DoubleDQN/model.py

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+#!/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)

codes/DoubleDQN/params.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-12-22 15:22:17
+LastEditor: John
+LastEditTime: 2020-12-22 15:26:09
+Discription: 
+Environment: 
+'''
+import datetime
+import os
+import argparse
+
+ALGO_NAME = 'Double DQN'
+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

codes/DoubleDQN/plot.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 16:30:09
+@LastEditor: John
+LastEditTime: 2020-12-22 15:24:31
+@Discription: 
+@Environment: python 3.7.7
+'''
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import os 
+from params import ALGO_NAME
+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 '+ALGO_NAME) 
+    plt.ylabel(ylabel)
+    plt.xlabel('episodes')
+    if save_fig:
+        plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
+    plt.show()
+
+
+    # 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) 

codes/DoubleDQN/utils.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-10-15 21:28:00
+LastEditor: John
+LastEditTime: 2020-10-15 21:50:30
+Discription: 
+Environment: 
+'''
+import os
+import numpy as np
+
+
+def save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path='./result'):
+    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 )