Update README

fluid/DeepQNetwork/README.md

@@ -17,43 +17,42 @@ Based on PaddlePaddle's next-generation API Fluid, the DQN model of deep reinfor
 
 # How to use
 ### Dependencies:
-    + python2.7
-    + gym
-    + tqdm
-    + opencv-python
-    + paddlepaddle-gpu>=0.12.0
-    + ale_python_interface
++ python2.7
++ gym
++ tqdm
++ opencv-python
++ paddlepaddle-gpu>=0.12.0
++ ale_python_interface
 
 ### Install Dependencies:
-    + Install PaddlePaddle:
++ Install PaddlePaddle:
     recommended to compile and install PaddlePaddle from source code
-    + Install other dependencies:
++ 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 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 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 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)
+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 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
-    ```
+# 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_cn.md

@@ -15,17 +15,17 @@
 
 # 使用教程
 ### 依赖:
-    + python2.7
-    + gym
-    + tqdm
-    + opencv-python
-    + paddlepaddle-gpu>=0.12.0
-    + ale_python_interface
++ python2.7
++ gym
++ tqdm
++ opencv-python
++ paddlepaddle-gpu>=0.12.0
++ ale_python_interface
 
 ### 下载依赖：
-    + 安装PaddlePaddle：
++ 安装PaddlePaddle：
     建议通过PaddlePaddle源码进行编译安装  
-    + 下载其它依赖：
++ 下载其它依赖：
     ```
     pip install -r requirement.txt
     pip install gym[atari]
@@ -33,25 +33,25 @@
     安装ale_python_interface可以参考：https://github.com/mgbellemare/Arcade-Learning-Environment
 
 ### 训练模型：
-    ```
-    # 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
+```
+# 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
 
-    # 训练DoubleDQN模型
-    python train.py --rom ./rom_files/pong.bin --use_cuda --alg DoubleDQN
+# 训练DoubleDQN模型
+python train.py --rom ./rom_files/pong.bin --use_cuda --alg DoubleDQN
 
-    # 训练DuelingDQN模型
-    python train.py --rom ./rom_files/pong.bin --use_cuda --alg DuelingDQN
-    ```
+# 训练DuelingDQN模型
+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)
+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)
 
 ### 测试模型：
-    ```
-    # 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
-
-    # 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
-    ```
+```
+# 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
+
+# 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
+```