南京航空航天大学学报（英文版）2023，Vol.40Issue(6)：627-640,14.DOI:10.16356/j.1005-1120.2023.06.001
基于虚拟自博弈多智能体近端优化策略的无人机对抗决策
UAV Confrontation Decision-Making Based on Fictitious Self-play Multi-agent Proximal Policy Optimization
摘要
Abstract
This paper addresses the confrontation decision-making problem of unmanned aerial vehicles(UAVs)based on fictitious self-play multi-agent proximal policy optimization.UAV confrontation relies on autonomous decision-making,enabling the UAV to generate action instructions based on environmental information.An innovative autonomous decision-making methodology for UAV confrontations is proposed within the context of red-blue air combat tasks.Initially,the current situation is evaluated by employing the relative angle between the missile attack area and the UAV.Following this,guided by the evaluated scenario,the design of state space,action space,and real-time reward feedback is implemented to streamline the training process.Subsequently,an advanced method is introduced for optimizing strategy through a virtual autonomous agent's proximity,aiming to derive the advantage function and average strategy from the experience buffer of training data.Ultimately,the efficacy and superiority of the proposed method are validated through simulations of UAVs engaging in red-blue countermeasure tasks.关键词
无人机/空战/多智能体近端优化策略/决策Key words
unmanned aerial vehicle(UAV)/air combat/multi-agent proximal policy optimization(MAPPO)/decision-making分类
信息技术与安全科学引用本文复制引用
王明明,张宝勇,吴冲,平原,齐俊桐..基于虚拟自博弈多智能体近端优化策略的无人机对抗决策[J].南京航空航天大学学报（英文版）,2023,40(6):627-640,14.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(No.62173242). （No.62173242）
