Update README

fluid/DeepQNetwork/README.md

@@ -19,15 +19,12 @@
     + gym
     + tqdm
     + opencv-python
-    + paddlepaddle-gpu==0.12.0 
+    + paddlepaddle-gpu>=0.12.0 
     + ale_python_interface
 
 + 下载依赖：
     + 安装PaddlePaddle
-    使用官方提供的PaddlePaddle编译环境和源码，通过源码编译安装
-        1. 下载源码：git clone https://github.com/PaddlePaddle/Paddle.git
-        2. 获取编译环境docker：docker pull docker.paddlepaddlehub.com/paddle:latest-dev
-        3. 在该环境中编译PaddlePaddle源码，并安装python包
+    建议通过PaddlePaddle源码进行编译安装    
     + 下载其它依赖
     ```
     pip install -r requirement.txt 

fluid/DeepQNetwork/README_English.md

+# Reproduce DQN, DoubleDQN, DuelingDQN model with fluid version of PaddlePaddle
+
++ DQN in:
+[Human-level Control Through Deep Reinforcement Learning](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
++ DoubleDQN in:
+[Deep Reinforcement Learning with Double Q-Learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12389)
++ DuelingDQN in:
+[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)
+
++ Pong game result
+![DQN result](assets/dqn.png)
+
+# How to use
++ Dependencies:
+    + python2.7
+    + gym
+    + tqdm
+    + opencv-python
+    + paddlepaddle-gpu>=0.12.0
+    + ale_python_interface
+
++ 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]
+    ```
+    Install ale_python_interface, can reference：https://github.com/mgbellemare/Arcade-Learning-Environment
+
+
++ Start Training:
+    ```
+    # To train a model for Pong game with gpu (use DQN model as default)
+    python train.py --rom ./rom_files/pong.bin --use_cuda
+
+    # To train a model for Pong with DoubleDQN
+    python train.py --rom ./rom_files/pong.bin --use_cuda --alg DoubleDQN
+
+    # To train a model for Pong with DuelingDQN
+    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)
+
++ Start Testing:
+    ```
+    # 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
+
+    # Play the game with visualization
+    python play.py --rom ./rom_files/pong.bin --use_cuda --model_path ./saved_model/DQN-pong --viz 0.01
+    ```