UAV swarm air combat maneuver decision-making method based on multi-agent reinforcement learning and transferring  

作　　者：Zhiqiang ZHENG Chen WEI Haibin DUAN 

机构地区：[1]State Key Laboratory of Virtual Reality Technology and Systems,School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083,China

出　　处：《Science China(Information Sciences)》2024年第8期45-62,共18页中国科学（信息科学）（英文版）

基　　金：supported by National Key R&D Program of China(Grant No.2023YFC3011001);National Natural Science Foundation of China(Grant Nos.U20B2071,62350048,T2121003).

摘　　要：During short-range air combat involving unmanned aircraft vehicle(UAV)swarms,UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs.This dual requirement of competition and cooperation presents a significant challenge in the field of unmanned air combat.In this paper,a method based on multi-agent reinforcement learning(MARL)is proposed to address this issue.An actor network containing three subnetworks that can handle different types of situational information is designed.Hence,the results from simpler one-on-one scenarios are leveraged to enhance the complex swarm air combat training process.Separate state spaces for local and global information are designed for the actor and critic networks.A detailed reward function is proposed to encourage participation.To prevent lazy participants in air combat,a reward assignment operation is applied to distribute these dense rewards.Simulation testing and ablation experiments demonstrate that both the transfer operation and reward assignment operation can effectively deal with the swarm air combat scenario,and reflect the effectiveness of the proposed method.

关 键 词：UAV swarm short-range air combat multi-agent reinforcement learning reward assignment transfer 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V279[自动化与计算机技术—控制科学与工程]