空天防御2024，Vol.7Issue(1)：24-31,8.
基于强化学习的多对多拦截目标分配方法
Reinforcement Learning-Based Target Assignment Method for Many-to-Many Interceptions
摘要
Abstract
Aiming at the issue of weapon target assignment for a many-to-many interception in the air confrontation environment,this study has proposed a multi-target intelligent assignment method based on reinforcement learning.Under the many-to-many interception engagement scenario,a mathematical model of target assignment was established based on the engagement posture evaluation.By introducing the concepts of target threat degree and interception effectiveness degree,the interception urgency of each target and the interception capability characterization of each interceptor were fully reflected,allowing a comprehensive evaluation of the engagement posture of the attacking and defending sides.Based on the target assignment model,the target assignment issue was built up using a Markov decision process and was trained to be solved by a reinforcement learning algorithm using deep Q-network.Relying on the self-learning and reward mechanism under environment interaction,the dynamic generation of optimal assignment schemes was effectively realized.A many-to-many interception scenario was created and its effectiveness was verified through mathematical simulation,and the result shows that the trained target assignment method satisfies the requirements of continuous and dynamic task assignment in many-to-many interception.关键词
武器目标分配/多目标拦截/态势评估/强化学习/深度Q网络Key words
weapon-target assignment/multi-target interception/situational evaluation/reinforcement learning/deep Q-network分类
军事科技引用本文复制引用
郭建国,胡冠杰,许新鹏,刘悦,曹晋..基于强化学习的多对多拦截目标分配方法[J].空天防御,2024,7(1):24-31,8.基金项目
国家自然科学基金(61973254,92271109,52272404) （61973254,92271109,52272404）
