modularized DQN

network.py

@@ -0,0 +1,31 @@
+import tensorflow as tf
+from common import *
+
+class Network:
+    def __init__(self, name, dim_in, dim_out, optimizer_fn, initializer=tf.random_normal_initializer()):
+        self.x = tf.placeholder(tf.float32, shape=(None, dim_in))
+        dim_hidden1 = 50
+        dim_hidden2 = 200
+        W1, b1, net1, phi1 = \
+            fully_connected(name, 'layer1', self.x, dim_in, dim_hidden1, initializer, tf.nn.relu)
+        W2, b2, net2, phi2 = \
+            fully_connected(name, 'layer2', phi1, dim_hidden1, dim_hidden2, initializer, tf.nn.relu)
+        W3, b3, net3, self.y = \
+            fully_connected(name, 'layer3', phi2, dim_hidden2, dim_out, initializer, tf.identity)
+        self.target = tf.placeholder(tf.float32, shape=(None, dim_out))
+        loss = 0.5 * tf.reduce_mean(tf.squared_difference(self.y, self.target))
+        self.variables = [W1, b1, W2, b2, W3, b3]
+        self.train_op = optimizer_fn(name).minimize(loss=loss)
+
+    def get_assign_ops(self, src_network):
+        assign_ops = []
+        for dst_var, src_var in zip(self.variables, src_network.variables):
+            assign_ops.append(dst_var.assign(src_var))
+        return assign_ops
+
+    def predict(self, sess, x):
+        y = sess.run(self.y, feed_dict={self.x: x})
+        return y
+
+    def learn(self, sess, x, target):
+        sess.run(self.train_op, feed_dict={self.x: x, self.target: target})