add dqn

codes/dqn/main.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:48:57
 @LastEditor: John
-@LastEditTime: 2020-07-20 23:02:16
+LastEditTime: 2020-08-22 18:02:56
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -14,26 +14,27 @@ import torch
 from dqn import DQN
 from plot import plot
 import argparse
+
 def get_args():
-    '''模型建立好之后只需要在这里调参
+    '''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=0.01, type=float)
-    parser.add_argument("--epsilon_decay", default=200, 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")
+                        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("--max_episodes", default=200, type=int)
+    parser.add_argument("--max_episodes", default=200, type=int) # 训练的最大episode数目
     parser.add_argument("--max_steps", default=200, type=int)
-    parser.add_argument("--target_update", default=2, 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=1, type=int,
                         help="when(every default 10 eisodes) to update target net ")
     config = parser.parse_args()
 
@@ -44,38 +45,34 @@ if __name__ == "__main__":
 
     cfg = get_args()
     # if gpu is to be used
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    env = gym.make('CartPole-v0').unwrapped
-    env.seed(1)
+    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)
+                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 = []
     for i_episode in range(1, cfg.max_episodes+1):
-        # Initialize the environment and state
-        state = env.reset()
+        state = env.reset() # reset环境状态
         ep_reward = 0
-        for t in range(1, cfg.max_steps+1):
-            # Select and perform an action
-            action = agent.select_action(state)
-            next_state, reward, done, _ = env.step(action)
+        for i_step in range(1, cfg.max_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
             ep_reward += reward
-            # Store the transition in memory
-            agent.memory.push(state,action,reward,next_state,done)
-            # Move to the next state
-            state = next_state
-            # Perform one step of the optimization (on the target network)
-            agent.update()
+            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
-
-        # Update the target network, copying all weights and biases in DQN
+        # 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), 'Explore: %.2f' % agent.epsilon)
+              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:
@@ -83,14 +80,17 @@ 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
     import os
     import numpy as np
     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")