This repo holds the codes for the paper "Learning Evasion Strategy in Pursuit-Evasion by Deep Q-Network, ICPR 2018".
To replicate the experiment results, a number of dependencies need to be installed, namely:
- LuaJIT and Torch 7.0
- nngraph
- Xitari (fork of the Arcade Learning Environment (Bellemare et al., 2013))
- AleWrap (a lua interface to Xitari) An install script for these dependencies is provided.
Two run scripts are provided: run_cpu and run_gpu. As the names imply, the former trains the DQN network using regular CPUs, while the latter uses GPUs (CUDA), which typically results in a significant speed-up.
The installation requires Linux with apt-get.
Note: In order to run the GPU version of DQN, you should additionally have the NVIDIA® CUDA® (version 5.5 or later) toolkit installed prior to the Torch installation below. This can be downloaded from https://developer.nvidia.com/cuda-toolkit and installation instructions can be found in http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux
To train the DQN, the following components must be installed:
- LuaJIT and Torch 7.0
- nngraph
- Xitari
- AleWrap
To install all of the above in a subdirectory called 'torch', it should be enough to run
./install_dependencies.sh
from the base directory of the package.
Note: The above install script will install the following packages via apt-get: build-essential, gcc, g++, cmake, curl, libreadline-dev, git-core, libjpeg-dev, libpng-dev, ncurses-dev, imagemagick, unzip
./run_gpu
Please cite the following paper if you feel this repository useful.
@inproceedings{PURSUITEVASION2018ICPR,
author = {Jiagang Zhu and
Wei Zou and
Zheng Zhu},
title = {Learning Evasion Strategy in Pursuit-Evasion by Deep Q-Network},
booktitle = {ICPR},
year = {2018},
}
For any question, please contact
Jiagang Zhu: zhujiagang2015@ia.ac.cn