add DQN_cnn

codes/DQN_cnn/model.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 12:18:12
+@LastEditor: John
+@LastEditTime: 2020-06-11 17:23:45
+@Discription: 
+@Environment: python 3.7.7
+'''
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CNN(nn.Module):
+
+    def __init__(self, h, w, n_outputs):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2)
+        self.bn1 = nn.BatchNorm2d(16)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
+        self.bn2 = nn.BatchNorm2d(32)
+        self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
+        self.bn3 = nn.BatchNorm2d(32)
+
+        # Number of Linear input connections depends on output of conv2d layers
+        # and therefore the input image size, so compute it.
+        def conv2d_size_out(size, kernel_size = 5, stride = 2):
+            return (size - (kernel_size - 1) - 1) // stride  + 1
+        convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
+        convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
+        linear_input_size = convw * convh * 32
+        self.head = nn.Linear(linear_input_size, n_outputs)
+
+    # Called with either one element to determine next action, or a batch
+    # during optimization. Returns tensor([[left0exp,right0exp]...]).
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.relu(self.bn3(self.conv3(x)))
+        return self.head(x.view(x.size(0), -1))