A hierarchical reinforcement learning method on multi UCAV air combat

Yabin Wang; Tianshu Jiang; Youjiang Li

doi:10.1117/12.2615268

15 October 2021 A hierarchical reinforcement learning method on multi UCAV air combat

Yabin Wang, Tianshu Jiang, Youjiang Li

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330K (2021) https://doi.org/10.1117/12.2615268
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

In the recent years, the unmanned combat aerial vehicle (UCAV) techniques is a hot topic of research. Many researches are studying how to use to fulfill missions and defend enemies based on simulation platforms. Different AI agents have been constructed to control virtual UCAVs to perform tasks on simulation platforms. Rule based AI heavily depends on human knowledge and lacks of flexibility. They cannot adapt to the changing environment. Reinforcement learning based AI has advantages over rule based AI as its depend less on human knowledge. In this paper a hierarchical reinforcement learning method is proposed on Multi-UCAV air combat based on simulation platform. The experiment results showed that the hierarchical approach can outperform state-of-the-art air combat method.

Citation Download Citation

Yabin Wang, Tianshu Jiang, and Youjiang Li "A hierarchical reinforcement learning method on multi UCAV air combat", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330K (15 October 2021); https://doi.org/10.1117/12.2615268

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