@inproceedings{2babf2b2905841ba9cb1ab950b0f0f4d,
title = "Maneuver Decision Making for Multi-aircraft Air Combat Based on Reinforcement Learning with Attention Mechanism",
abstract = "This paper studies the problem of maneuver decision-making for multi-aircraft air combat. First, considering pertinent air combat scenarios, the aircraft model and the air combat attack zone are established. Then, the Attention Mechanism (AM) is introduced to improve the convergence speed of the Proximal Policy Optimization (PPO) algorithm. Moreover, we design a maneuver decision-making model for multi-aircraft air combat based on the proposed AM-PPO algorithm. Finally, through 3v3 air combat simulation experiments, this paper validates the AM-PPO algorithm against the classical PPO algorithm in terms of convergence speed and stability. The simulation results further illustrate that the proposed algorithm can effectively control the aircraft to execute strategic maneuvers against opponents while avoiding friendly aircraft.",
keywords = "Attention Mechanism, Deep reinforcement learning, maneuver decision",
author = "Peida Li and Xiaoduo Li and Liang Han",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2240-5\_57",
language = "英语",
isbn = "9789819622399",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "585--594",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 11",
address = "德国",
}