update DQN

codes/dqn/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/Users/jj/anaconda3/envs/py37/bin/python"
+}

codes/dqn/README.md

@@ -0,0 +1,24 @@
+python 3.7.9
+
+pytorch 1.6.0
+
+tensorboard 2.3.0 
+
+torchvision 0.7.0 
+
+
+train: 
+
+```python
+python main.py 
+```
+
+eval: 
+
+```python
+python main.py --train 0 
+```
+
+```python
+tensorboard --logdir logs 
+```

codes/dqn/agent.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:50:49
 @LastEditor: John
-LastEditTime: 2020-10-07 17:32:18
+LastEditTime: 2020-10-15 21:56:21
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -13,8 +13,6 @@ LastEditTime: 2020-10-07 17:32:18
 '''
 
 
-
-
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -30,7 +28,7 @@ class DQN:
         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
+        # e-greedy策略相关参数
         self.epsilon = 0
         self.epsilon_start = epsilon_start
         self.epsilon_end = epsilon_end
@@ -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) * \
+        if train:
-            math.exp(-1. * self.actions_count / self.epsilon_decay)
+            self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
-        self.actions_count += 1
+                math.exp(-1. * self.actions_count / self.epsilon_decay)
-        if random.random() > self.epsilon:
+            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
+                    # 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
+                    # 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
-                state = torch.tensor(
+                    state = torch.tensor(
-                    [state], device=self.device, dtype=torch.float32)
+                        [state], device='cpu', dtype=torch.float32)
-                # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
+                    # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
-                q_value = self.policy_net(state)
+                    q_value = self.target_net(state)
-                # tensor.max(1)返回每行的最大值以及对应的下标，
+                    # 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]))
+                    # 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
+                    # 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()  
+                    action = q_value.max(1)[1].item() 
-        else:
+            return action
-            action = random.randrange(self.n_actions)
-        return action
-
     def update(self):
 
         if len(self.memory) < self.batch_size:
@@ -113,8 +120,9 @@ 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():
+    def save_model(self,path):
-        pass
+        torch.save(self.target_net.state_dict(), path)
-    def load_model():
+
-        pass
+    def load_model(self,path):
+        self.target_net.load_state_dict(torch.load(path))  

codes/dqn/main.py

@@ -5,20 +5,28 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:48:57
 @LastEditor: John
-LastEditTime: 2020-08-22 18:02:56
+LastEditTime: 2020-10-15 22:00:28
 @Discription: 
 @Environment: python 3.7.7
 '''
 import gym
 import torch
-from dqn import DQN
+from agent import DQN
-from plot import plot
 import argparse
+from torch.utils.tensorboard import SummaryWriter
+import datetime
+import os
+from utils import save_results
+
+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,
@@ -31,20 +39,19 @@ def get_args():
 
     parser.add_argument("--batch_size", default=32, type=int,
                         help="batch size of memory sampling")
-    parser.add_argument("--max_episodes", default=200, type=int) # 训练的最大episode数目
+    parser.add_argument("--train_eps", default=200, type=int) # 训练的最大episode数目
-    parser.add_argument("--max_steps", default=200, type=int)
+    parser.add_argument("--train_steps", default=200, type=int)
-    # 将目标网络的更新频率改为1就是普通的dqn，大于1就是double dqn
+    parser.add_argument("--target_update", default=2, type=int,
-    parser.add_argument("--target_update", default=1, type=int,
+                        help="when(every default 2 eisodes) to update target net ") # 更新频率
-                        help="when(every default 10 eisodes) to update target net ")
+
+    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')
-if __name__ == "__main__":
-
-    cfg = get_args()
-    # if gpu is to be used
     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
@@ -55,11 +62,13 @@ if __name__ == "__main__":
     rewards = []
     moving_average_rewards = []
     ep_steps = []
-    for i_episode in range(1, cfg.max_episodes+1):
+    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.max_steps+1):
+        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
@@ -80,17 +89,68 @@ if __name__ == "__main__":
         else:
             moving_average_rewards.append(
                 0.9*moving_average_rewards[-1]+0.1*ep_reward)
-    # 存储reward等相关结果
+        writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
-    import os
+        writer.add_scalar('steps_of_each_episode',
-    import numpy as np
+                          ep_steps[-1], i_episode)
-    output_path = os.path.dirname(__file__)+"/result/"
+    writer.close()
-    # 检测是否存在文件夹
+    print('Complete training！')
-    if not os.path.exists(output_path):
+    ''' 保存模型 '''
-        os.mkdir(output_path)
+    if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
-    np.save(output_path+"rewards.npy", rewards)
+        os.mkdir(SAVED_MODEL_PATH)
-    np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
+    agent.save_model(SAVED_MODEL_PATH+'checkpoint.pth')
-    np.save(output_path+"steps.npy", ep_steps)
+    print('model saved！')
-    print('Complete！')
+    '''存储reward等相关结果'''
-    plot(rewards)
+    save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
-    plot(moving_average_rewards, ylabel="moving_average_rewards")
+    
-    plot(ep_steps, ylabel="steps_of_each_episode")
+
+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=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/dqn/plot.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-11 16:30:09
 @LastEditor: John
-LastEditTime: 2020-10-07 20:57:22
+LastEditTime: 2020-10-15 22:01:50
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -14,19 +14,45 @@ import seaborn as sns
 import numpy as np
 import os 
 
-def plot(item,ylabel='rewards'):
+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')
-    plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
+    if save_fig:
+        plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
     plt.show()
+
+# def plot(item,ylabel='rewards'):
+#     
+#     df = pd.DataFrame(dict(time=np.arange(len(item)),value=item))
+#     g = sns.relplot(x="time", y="value", kind="line", data=df)
+#     # g.fig.autofmt_xdate()
+#     # sns.lineplot(time=time, data=item, color="r", condition="behavior_cloning")
+#     # # sns.tsplot(time=time, data=x2, color="b", condition="dagger")
+#     # plt.ylabel("Reward")
+#     # plt.xlabel("Iteration Number")
+#     # plt.title("Imitation Learning")
+
+    # plt.show()
 if __name__ == "__main__":
 
-    output_path = os.path.dirname(__file__)+"/result/"
+    output_path = os.path.split(os.path.abspath(__file__))[0]+"/result/"
-    rewards=np.load(output_path+"rewards.npy", )
+    tag = 'train'
-    moving_average_rewards=np.load(output_path+"moving_average_rewards.npy",)
+    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')
+    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/dqn/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 )