diff --git a/fluid/DeepQNetwork/README.md b/fluid/DeepQNetwork/README.md
index 54534a368df76d6c478a78b897491da432c35009..47d97ae487c3437f6b5c87d8ba7d1753f3929d77 100644
--- a/fluid/DeepQNetwork/README.md
+++ b/fluid/DeepQNetwork/README.md
@@ -13,7 +13,7 @@ Based on PaddlePaddle's next-generation API Fluid, the DQN model of deep reinfor
 ### [Atari games introduction](https://gym.openai.com/envs/#atari)
 
 ### Pong game result
-The average game rewards that can be obtained for the three models as the number of training steps changes during the training are as follows:
+The average game rewards that can be obtained for the three models as the number of training steps changes during the training are as follows(about 3 hours/1 Million steps):
 ![DQN result](assets/dqn.png)
 
 ## How to use
diff --git a/fluid/DeepQNetwork/README_cn.md b/fluid/DeepQNetwork/README_cn.md
index f6a15b85c8c235f7d426793c4a420b50e015639e..dcbd8e9e6f8fdc370bcf82254effbf10e4450a69 100644
--- a/fluid/DeepQNetwork/README_cn.md
+++ b/fluid/DeepQNetwork/README_cn.md
@@ -11,7 +11,7 @@
 ### [Atari游戏介绍](https://gym.openai.com/envs/#atari)
 
 ### Pong游戏训练结果
-三个模型在训练过程中随着训练步数的变化，能得到的平均游戏奖励如下图所示：
+三个模型在训练过程中随着训练步数的变化，能得到的平均游戏奖励如下图所示（大概3小时每1百万步）：
 ![DQN result](assets/dqn.png)
 
 ## 使用教程