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.

Installation instructions

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

Training the DQN

./run_gpu

Citation

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},
}

Contact

For any question, please contact

Jiagang Zhu: [email protected]