diff --git a/AlphaGo/model.py b/AlphaGo/model.py
index 9106828..5037173 100644
--- a/AlphaGo/model.py
+++ b/AlphaGo/model.py
@@ -99,19 +99,19 @@ class ResNet(object):
         self.sess = multi_gpu.create_session()
         self.sess.run(tf.global_variables_initializer())
         if black_checkpoint_path is not None:
-            ckpt_file = tf.train.latest_checkpoint(black_checkpoint_path)
-            if ckpt_file is not None:
-                print('Restoring model from {}...'.format(ckpt_file))
-                self.black_saver.restore(self.sess, ckpt_file)
+            self.black_ckpt_file = tf.train.latest_checkpoint(black_checkpoint_path)
+            if self.black_ckpt_file is not None:
+                print('Restoring model from {}...'.format(self.black_ckpt_file))
+                self.black_saver.restore(self.sess, self.black_ckpt_file)
                 print('Successfully loaded')
             else:
                 raise ValueError("No model in path {}".format(black_checkpoint_path))
 
         if white_checkpoint_path is not None:
-            ckpt_file = tf.train.latest_checkpoint(white_checkpoint_path)
-            if ckpt_file is not None:
-                print('Restoring model from {}...'.format(ckpt_file))
-                self.white_saver.restore(self.sess, ckpt_file)
+            self.white_ckpt_file = tf.train.latest_checkpoint(white_checkpoint_path)
+            if self.white_ckpt_file is not None:
+                print('Restoring model from {}...'.format(self.white_ckpt_file))
+                self.white_saver.restore(self.sess, self.white_ckpt_file)
                 print('Successfully loaded')
             else:
                 raise ValueError("No model in path {}".format(white_checkpoint_path))
@@ -124,6 +124,9 @@ class ResNet(object):
         self.training_data = {'states': deque(maxlen=self.window_length), 'probs': deque(maxlen=self.window_length),
                               'winner': deque(maxlen=self.window_length), 'length': deque(maxlen=self.window_length)}
 
+        # training or not
+        self.training = False
+
     def _build_network(self, scope, residual_block_num):
         """
         build the network
@@ -184,6 +187,22 @@ class ResNet(object):
             return self.sess.run([self.white_prob, self.white_v],
                                  feed_dict={self.x: eval_state, self.is_training: False})
 
+    def check_latest_model(self):
+        if self.training:
+            black_ckpt_file = tf.train.latest_checkpoint(self.save_path + "black/")
+            if self.black_ckpt_file != black_ckpt_file:
+                self.black_ckpt_file = black_ckpt_file
+                print('Loading model from {}...'.format(self.black_ckpt_file))
+                self.black_saver.restore(self.sess, self.black_ckpt_file)
+                print('Black Model Updated!')
+
+            white_ckpt_file = tf.train.latest_checkpoint(self.save_path + "white/")
+            if self.white_ckpt_file != white_ckpt_file:
+                self.white_ckpt_file = white_ckpt_file
+                print('Loading model from {}...'.format(self.white_ckpt_file))
+                self.white_saver.restore(self.sess, self.white_ckpt_file)
+                print('White Model Updated!')
+
     def _history2state(self, history, color):
         """
         convert the history to the state we need
@@ -215,6 +234,7 @@ class ResNet(object):
         :param target: a string, which to optimize, can only be "both", "black" and "white"
         :param mode: a string, how to optimize, can only be "memory" and "file"
         """
+        self.training = True
         if mode == 'memory':
             pass
         if mode == 'file':
diff --git a/AlphaGo/play.py b/AlphaGo/play.py
index 5977f06..a000b78 100644
--- a/AlphaGo/play.py
+++ b/AlphaGo/play.py
@@ -61,6 +61,7 @@ if __name__ == '__main__':
         while True:
         #while game_num < evaluate_rounds:
             start_time = time.time()
+            game.model.check_latest_model()
             num = 0
             pass_flag = [False, False]
             print("Start game {}".format(game_num))