diff --git a/examples/dqn_example.py b/examples/dqn_example.py
index 0a5c084..4fbe466 100644
--- a/examples/dqn_example.py
+++ b/examples/dqn_example.py
@@ -53,7 +53,7 @@ if __name__ == '__main__':
     action = tf.placeholder(dtype=tf.int32, shape=[None]) # batch of integer actions
     target = tf.placeholder(dtype=tf.float32, shape=[None]) # target value for DQN
 
-    dqn_loss = losses.dqn_loss(action, target, pi) # TongzhengRen
+    dqn_loss = losses.dqn_loss(action, target, q_net) # TongzhengRen
 
     total_loss = dqn_loss
     optimizer = tf.train.AdamOptimizer(1e-3)
@@ -61,7 +61,7 @@ if __name__ == '__main__':
 
     # 3. define data collection
     training_data = Replay(env, q_net, advantage_estimation.qlearning_target(target_net)) #
-                                                             # ShihongSong: Replay(env, pi, advantage_estimation.qlearning_target(target_network)), use your ReplayMemory, interact as follows. Simplify your advantage_estimation.dqn to run before YongRen's DQN
+                                                             # ShihongSong: Replay(env, q_net, advantage_estimation.qlearning_target(target_network)), use your ReplayMemory, interact as follows. Simplify your advantage_estimation.dqn to run before YongRen's DQN
     # maybe a dict to manage the elements to be collected
 
     # 4. start training