add dqn

codes/dqn/model.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:47:02
 @LastEditor: John
-@LastEditTime: 2020-06-14 11:23:04
+LastEditTime: 2020-08-19 16:55:54
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -14,17 +14,17 @@ import torch.nn.functional as F
 
 class FCN(nn.Module):
     def __init__(self, n_states=4, n_actions=18):
-        """
-        Initialize a deep Q-learning network for testing algorithm
-            n_states: number of features of input.
-            n_actions: number of action-value to output, one-to-one correspondence to action in game.
+        """ 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)
-        self.fc2 = nn.Linear(128, 128)
-        self.fc3 = nn.Linear(128, n_actions)
+        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):
-        x = F.relu(self.fc1(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)