update DoubleDQN

codes/DoubleDQN/README.md

@@ -0,0 +1,33 @@
+## 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

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:50:49
 @LastEditor: John
-LastEditTime: 2020-09-01 22:54:02
+LastEditTime: 2020-12-22 14:44:46
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -13,8 +13,6 @@ LastEditTime: 2020-09-01 22:54:02
 '''
 
 
-
-
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -46,32 +44,41 @@ class DQN:
         self.loss = 0
         self.memory = ReplayBuffer(memory_capacity)
 
-    def select_action(self, state):
+    def choose_action(self, state, train=True):
         '''选择动作
-        Args:
-            state [array]: [description]
-        Returns:
-            action [array]: [description]
         '''
-        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:
+        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=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
-
+                    # 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:
@@ -93,19 +100,25 @@ class DQN:
             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)
-        # 关于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(
+        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 expected_q_value
+        # 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
-        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
+        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
@@ -113,9 +126,9 @@ class DQN:
         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.policy_net.load_state_dict(torch.load(path))
+        self.target_net.load_state_dict(torch.load(path))  

codes/DoubleDQN/main.py

@@ -5,45 +5,21 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:48:57
 @LastEditor: John
-LastEditTime: 2020-09-01 22:54:23
+LastEditTime: 2020-12-22 15:39:46
 @Discription: 
 @Environment: python 3.7.7
 '''
 import gym
 import torch
-from dqn import DQN
-from plot import plot
-import argparse
+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 get_args():
-    '''模型参数
-    '''
-    parser = argparse.ArgumentParser()
-    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=0.05, 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) # 训练每个episode的长度
-    parser.add_argument("--eval_eps", default=200, 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
-    parser.add_argument("--target_update", default=2, type=int,
-                        help="when(every default 10 eisodes) to update target net ")
-    config = parser.parse_args()
-
-    return config
-
-def train():
-    cfg = get_args()
-    # if gpu is to be used
+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
@@ -54,11 +30,13 @@ def train():
     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.select_action(state) # 根据当前环境state选择action
+            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
@@ -79,54 +57,48 @@ def train():
         else:
             moving_average_rewards.append(
                 0.9*moving_average_rewards[-1]+0.1*ep_reward)
-    import os
-    import numpy as np
-    save_path = os.path.dirname(__file__)+"/saved_model/"
-    if not os.path.exists(save_path):
-        os.mkdir(save_path)
-    agent.save_model(save_path+'checkpoint.pth')
-    # 存储reward等相关结果
-    output_path = os.path.dirname(__file__)+"/result/"
-    # 检测是否存在文件夹
-    if not os.path.exists(output_path):
-        os.mkdir(output_path)
-    np.save(output_path+"rewards.npy", rewards)
-    np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
-    np.save(output_path+"steps.npy", ep_steps)
-    print('Complete！')
-    plot(rewards)
-    plot(moving_average_rewards, ylabel="moving_average_rewards")
-    plot(ep_steps, ylabel="steps_of_each_episode")
+        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 = get_args()
-    # if gpu is to be used
+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, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
+    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)
-    import os
-    save_path = os.path.dirname(__file__)+"/saved_model/"
-    if not os.path.exists(save_path):
-        os.mkdir(save_path)
-    agent.load_model(save_path+'checkpoint.pth')
+    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
+        state = env.reset()  # reset环境状态
         ep_reward = 0
         for i_step in range(1, cfg.eval_steps+1):
-            action = agent.select_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
+            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
+            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,' Explore: %.2f' % agent.epsilon)
+              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
@@ -135,9 +107,20 @@ def eval():
         else:
             moving_average_rewards.append(
                 0.9*moving_average_rewards[-1]+0.1*ep_reward)
-    plot(rewards,save_fig=False)
-    plot(moving_average_rewards, ylabel="moving_average_rewards",save_fig=False)
-    plot(ep_steps, ylabel="steps_of_each_episode",save_fig=False)
+                
+        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__":
-    # train()
-    eval()
+    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

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-10 15:27:16
 @LastEditor: John
-@LastEditTime: 2020-06-14 11:36:24
+LastEditTime: 2020-12-22 12:56:27
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -15,21 +15,27 @@ import numpy as np
 class ReplayBuffer:
     
     def __init__(self, capacity):
-        self.capacity = 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/params.py

@@ -0,0 +1,48 @@
+#!/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

@@ -0,0 +1,48 @@
+#!/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 )

codes/double_dqn/plot.py

@@ -1,34 +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: 2020-09-01 22:46:43
-@Discription: 
-@Environment: python 3.7.7
-'''
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import numpy as np
-import os 
-
-def plot(item,ylabel='rewards',save_fig = True):
-    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.dirname(__file__)+"/result/"
-    rewards=np.load(output_path+"rewards.npy", )
-    moving_average_rewards=np.load(output_path+"moving_average_rewards.npy",)
-    plot(rewards)
-    plot(moving_average_rewards,ylabel='moving_average_rewards')