@inproceedings{f9ef6d03f4834af399866997fc990654,
title = "Air Combat Strategies Generation of CGF Based on MADDPG and Reward Shaping",
abstract = "The intelligence of the computer-generated force (CGF) is one of the important problems in air combat simulation. The air combat of CGF is modeled as a two-player zero-sum Markov game. An air combat strategies generation method of CGF is proposed to use a multi-agent deep deterministic policy gradient (MADDPG) algorithm. This paper proposes a potential-based reward shaping method to improve the efficiency of the air combat policy generation algorithm. Finally, the efficiency of the air combat policy generation algorithm and the intelligence level of the resulting policy is verified through simulation experiments. The simulation results show that this method has good convergence and better air combat performance with the strategy obtained by the DDPG algorithm.",
keywords = "Air combat strategies, CFG, Computer simulation, MADDPG, Reward shaping",
author = "Weiren Kong and Deyun Zhou and Zhen Yang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020 ; Conference date: 10-07-2020 Through 12-07-2020",
year = "2020",
month = jul,
doi = "10.1109/CVIDL51233.2020.000-7",
language = "英语",
series = "Proceedings - 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "651--655",
booktitle = "Proceedings - 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020",
}