引用本文:黄卓,徐振,郭健,陈庆伟,吴潇瑞.基于分区间强化学习的集群导弹快速任务分配[J].控制理论与应用,2023,40(6):1129~1139.[点击复制]
HUANG Zhuo,XU Zhen,GUO Jian,CHEN Qing-wei,WU Xiao-rui.Fast task allocation for missile swarm based on sectioned reinforcement learning[J].Control Theory and Technology,2023,40(6):1129~1139.[点击复制]
基于分区间强化学习的集群导弹快速任务分配
Fast task allocation for missile swarm based on sectioned reinforcement learning
摘要点击 1810  全文点击 559  投稿时间:2022-02-17  修订日期:2023-04-20
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DOI编号  10.7641/CTA.2023.20119
  2023,40(6):1129-1139
中文关键词  集群导弹  任务分配  不确定性  分区间强化学习
英文关键词  missile swarm  task allocation  uncertainty  sectioned reinforcement learning
基金项目  国防基础科研项目(JCKY2021606B002), 江苏省六大人才高峰项目(GDZB–027), 国家自然科学基金项目(U20B2056)
作者单位E-mail
黄卓 南京理工大学 huangzhuo@njust.edu.cn 
徐振 南京理工大学  
郭健* 南京理工大学 auto90092021@163.com 
陈庆伟 南京理工大学  
吴潇瑞 南京理工大学  
中文摘要
      针对集群导弹在线任务分配面临的环境不确定、耗时过长等问题, 本文研究了一种基于分区间强化学习的 集群导弹快速任务分配算法. 首先, 建立集群导弹的综合攻防性能模型, 并将存在环境不确定性的集群导弹任务分 配问题表述为马尔可夫决策过程. 其次, 针对该过程采用分区间强化学习, 通过将搜索空间划分成若干个子区间, 降低搜索维度, 加快算法的收敛过程, 并通过理论证明给出了最优区间划分依据. 最后, 通过3组仿真实验, 分别从 收敛速度、不确定条件下的寻优能力以及导弹和目标数量可变情况下的决策能力3个方面, 验证了所提算法的快速 性和优化性能.
英文摘要
      Aiming at the problems of uncertain environment and long time-consuming in online task allocation of missile swarm, this paper studies a fast task allocation algorithm of missile swarm based on the sectioned reinforcement learning. Firstly, the comprehensive attack and defense performance model of missile swarm is established, and the task allocation problem of missile swarm in the presence of environment uncertainty is expressed as the Markov decision process. Secondly, the sectioned reinforcement learning is adopted for this process. By dividing the search space into several subintervals, the search dimension is reduced and the convergence process of the algorithm is accelerated. In addition, the basis for optimal interval division is given through theoretical proof. Finally, through three groups of simulation experiments, the rapidity and the optimization performance of the proposed algorithm are verified from three aspects: convergence speed, optimization ability under uncertain conditions, and decision-making ability under variable number of missiles and targets.