提交 de84714e 编写于 作者: Z zenghsh3

Update README

上级 4a9aed3d
......@@ -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
```
......@@ -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
```
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