Cooperative Strike Target Assignment Algorithm Based on Deep Reinforcement Learning

Weiwei Bian, Chunguang Wang, Kuihua Huang, Yanxiang Jia, Chan Liu, Ying Mi
{"title":"Cooperative Strike Target Assignment Algorithm Based on Deep Reinforcement Learning","authors":"Weiwei Bian, Chunguang Wang, Kuihua Huang, Yanxiang Jia, Chan Liu, Ying Mi","doi":"10.1109/ICCSI55536.2022.9970699","DOIUrl":null,"url":null,"abstract":"Due to the complexity of the environment and the dynamic change of the target, the relationship between the target and the missile is diversified, with randomness, fuzziness and uncertainty. In order to improve the timeliness of command decisions and the accuracy of interception strategies, a deep reinforcement learning model is constructed to optimize the decision loss function, obtain the optimal target allocation results, and achieve effective coordination of strike firepower. The simulation results show that it is feasible to apply the deep reinforcement learning method to cooperative strike target allocation decision.","PeriodicalId":421514,"journal":{"name":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSI55536.2022.9970699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Due to the complexity of the environment and the dynamic change of the target, the relationship between the target and the missile is diversified, with randomness, fuzziness and uncertainty. In order to improve the timeliness of command decisions and the accuracy of interception strategies, a deep reinforcement learning model is constructed to optimize the decision loss function, obtain the optimal target allocation results, and achieve effective coordination of strike firepower. The simulation results show that it is feasible to apply the deep reinforcement learning method to cooperative strike target allocation decision.
基于深度强化学习的协同打击目标分配算法
由于环境的复杂性和目标的动态变化,目标与导弹之间的关系是多样化的,具有随机性、模糊性和不确定性。为了提高指挥决策的时效性和拦截策略的准确性,构建了深度强化学习模型,对决策损失函数进行优化,得到最优目标分配结果,实现打击火力的有效协调。仿真结果表明,将深度强化学习方法应用于协同打击目标分配决策是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信