智能系统学报2025，Vol.20Issue(3)：548-556,9.DOI:10.11992/tis.202408008
基于分层多智能体强化学习的多无人机视距内空战
Multi-UAV within-visual-range air combat based on hierarchical multiagent reinforcement learning
雍宇晨 1李子豫 2董琦3
作者信息
- 1. 东南大学软件学院,江苏南京 211189||中国电科电子科学研究院,北京 100041
- 2. 东南大学信息科学与工程学院,江苏南京 210096
- 3. 中国电科电子科学研究院,北京 100041
- 折叠
摘要
Abstract
To improve the autonomous maneuvering decision-making capabilities of unmanned aerial vehicles(UAVs)in within-visual-range air combat,a hierarchical decision network framework based on self-play theory(SP)and multia-gent reinforcement learning(MARL)is proposed in this paper.A multi-UAV dogfight scenario is studied by combining SP and an MARL algorithm.The complex air combat task is divided into upper-level missile strike tasks and lower-level flight tracking tasks,which effectively reduces the fuzziness of tactical action and improves the autonomous maneuver-ing decision-making ability in a multi-UAV dogfight scenario.In addition,through an innovative reward function design and by adopting the SP method,the algorithm reduces the meaningless exploration of an agent due to the large battle-field environment.Simulation results show that this algorithm can help agents learn basic flight tactics and advanced combat tactics and has better defensive and offensive capabilities compared with other multiagent air combat algorithms.关键词
视距内空战/缠斗/自主机动决策/自博弈/分层强化学习/多智能体博弈/分层决策网络/奖励函数设计Key words
air combat within visual range/dogfight/autonomous decision-making/self-play/hierarchical reinforce-ment learning/multi-intelligent body game/hierarchical decision networks/reward function design分类
信息技术与安全科学引用本文复制引用
雍宇晨,李子豫,董琦..基于分层多智能体强化学习的多无人机视距内空战[J].智能系统学报,2025,20(3):548-556,9.
