Update README of English version

fluid/DeepQNetwork/README.md

-# 基于PaddlePaddle的Fluid版本复现DQN, DoubleDQN, DuelingDQN三个模型
-基于Paddle下一代API fluid复现了深度强化学习领域的DQN模型，在经典的Atari 游戏上复现了论文同等水平的指标，模型接收游戏的图像作为输入，采用端到端的模型直接预测下一步要执行的控制信号，本仓库一共包含以下3类模型。
-+ DQN模型： 
+[中文版](README_cn.md)
+
+# Reproduce DQN, DoubleDQN, DuelingDQN model with Fluid version of PaddlePaddle
+Based on PaddlePaddle's next-generation API Fluid, the DQN model of deep reinforcement learning is reproduced, and the same level of indicators of the paper is reproduced in the classic Atari game. The model receives the image of the game as input, and uses the end-to-end model to directly predict the next step. The repository contains the following three types of models.
++ DQN in
 [Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
-+ DoubleDQN模型：
++ DoubleDQN in:
 [Deep Reinforcement Learning with Double Q-Learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12389)
-+ DuelingDQN模型：
++ DuelingDQN in:
 [Dueling Network Architectures for Deep Reinforcement Learning](http://proceedings.mlr.press/v48/wangf16.html)
 
-# 模型效果：Atari游戏表现
-## [Atari游戏介绍](https://gym.openai.com/envs/#atari)
+# Atari benchmark & performance
+## [Atari games introduction](https://gym.openai.com/envs/#atari)
 
-+ Pong游戏训练结果
++ Pong game result
 ![DQN result](assets/dqn.png)
 
-# 使用教程
-+ 依赖:
+# How to use
++ Dependencies:
     + python2.7
     + gym
     + tqdm
     + opencv-python
-    + paddlepaddle-gpu>=0.12.0 
+    + paddlepaddle-gpu>=0.12.0
     + ale_python_interface
 
-+ 下载依赖：
-    + 安装PaddlePaddle
-    建议通过PaddlePaddle源码进行编译安装    
-    + 下载其它依赖
++ Install Dependencies:
+    + Install PaddlePaddle
+    Recommended to compile and install PaddlePaddle from source code
+    + Install other dependencies:
     ```
     pip install -r requirement.txt 
     pip install gym[atari]
     ```
-    安装ale_python_interface可以参考：https://github.com/mgbellemare/Arcade-Learning-Environment
+    Install ale_python_interface, can reference：https://github.com/mgbellemare/Arcade-Learning-Environment
+
 
-+ 训练模型：
++ Start Training:
     ```
-    # 使用GPU训练Pong游戏（默认使用DQN模型）
+    # To train a model for Pong game with gpu (use DQN model as default)
     python train.py --rom ./rom_files/pong.bin --use_cuda
 
-    # 训练DoubleDQN模型
+    # To train a model for Pong with DoubleDQN
     python train.py --rom ./rom_files/pong.bin --use_cuda --alg DoubleDQN
 
-    # 训练DuelingDQN模型
+    # To train a model for Pong with DuelingDQN
     python train.py --rom ./rom_files/pong.bin --use_cuda --alg DuelingDQN
     ```
 
-    训练更多游戏，可以下载游戏rom从[这里](https://github.com/openai/atari-py/tree/master/atari_py/atari_roms)
+    To train more games, can install more rom files from [here](https://github.com/openai/atari-py/tree/master/atari_py/atari_roms)
 
-+ 测试模型： 
++ Start Testing:
     ```
-    # Play the game with saved model and calculate the average rewards
-    # 使用训练过程中保存的最好模型玩游戏，以及计算平均奖励（rewards） 
+    # Play the game with saved best model and calculate the average rewards
     python play.py --rom ./rom_files/pong.bin --use_cuda --model_path ./saved_model/DQN-pong
 
-    # 以可视化的形式来玩游戏
+    # Play the game with visualization
     python play.py --rom ./rom_files/pong.bin --use_cuda --model_path ./saved_model/DQN-pong --viz 0.01
     ```

fluid/DeepQNetwork/README_English.md → fluid/DeepQNetwork/README_cn.md

-# Reproduce DQN, DoubleDQN, DuelingDQN model with fluid version of PaddlePaddle
-
-+ DQN in:
+# 基于PaddlePaddle的Fluid版本复现DQN, DoubleDQN, DuelingDQN三个模型
+基于PaddlePaddle下一代API Fluid复现了深度强化学习领域的DQN模型，在经典的Atari 游戏上复现了论文同等水平的指标，模型接收游戏的图像作为输入，采用端到端的模型直接预测下一步要执行的控制信号，本仓库一共包含以下3类模型。
++ DQN模型： 
 [Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
-+ DoubleDQN in:
++ DoubleDQN模型：
 [Deep Reinforcement Learning with Double Q-Learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12389)
-+ DuelingDQN in:
++ DuelingDQN模型：
 [Dueling Network Architectures for Deep Reinforcement Learning](http://proceedings.mlr.press/v48/wangf16.html)
 
-# Atari benchmark & performance
-## [Atari games introduction](https://gym.openai.com/envs/#atari)
+# 模型效果：Atari游戏表现
+## [Atari游戏介绍](https://gym.openai.com/envs/#atari)
 
-+ Pong game result
++ Pong游戏训练结果
 ![DQN result](assets/dqn.png)
 
-# How to use
-+ Dependencies:
+# 使用教程
++ 依赖:
     + python2.7
     + gym
     + tqdm
     + opencv-python
-    + paddlepaddle-gpu>=0.12.0
+    + paddlepaddle-gpu>=0.12.0 
     + ale_python_interface
 
-+ Install Dependencies:
-    + Install PaddlePaddle
-    Recommended to compile and install PaddlePaddle from source code
-    + Install other dependencies:
++ 下载依赖：
+    + 安装PaddlePaddle
+    建议通过PaddlePaddle源码进行编译安装    
+    + 下载其它依赖
     ```
     pip install -r requirement.txt 
     pip install gym[atari]
     ```
-    Install ale_python_interface, can reference：https://github.com/mgbellemare/Arcade-Learning-Environment
-
+    安装ale_python_interface可以参考：https://github.com/mgbellemare/Arcade-Learning-Environment
 
-+ Start Training:
++ 训练模型：
     ```
-    # To train a model for Pong game with gpu (use DQN model as default)
+    # 使用GPU训练Pong游戏（默认使用DQN模型）
     python train.py --rom ./rom_files/pong.bin --use_cuda
 
-    # To train a model for Pong with DoubleDQN
+    # 训练DoubleDQN模型
     python train.py --rom ./rom_files/pong.bin --use_cuda --alg DoubleDQN
 
-    # To train a model for Pong with DuelingDQN
+    # 训练DuelingDQN模型
     python train.py --rom ./rom_files/pong.bin --use_cuda --alg DuelingDQN
     ```
 
-    To train more games, can install more rom files from [here](https://github.com/openai/atari-py/tree/master/atari_py/atari_roms)
+    训练更多游戏，可以下载游戏rom从[这里](https://github.com/openai/atari-py/tree/master/atari_py/atari_roms)
 
-+ Start Testing:
++ 测试模型： 
     ```
-    # Play the game with saved best model and calculate the average rewards
+    # Play the game with saved model and calculate the average rewards
+    # 使用训练过程中保存的最好模型玩游戏，以及计算平均奖励（rewards） 
     python play.py --rom ./rom_files/pong.bin --use_cuda --model_path ./saved_model/DQN-pong
 
-    # Play the game with visualization
+    # 以可视化的形式来玩游戏
     python play.py --rom ./rom_files/pong.bin --use_cuda --model_path ./saved_model/DQN-pong --viz 0.01
     ```