{"title":"利用改进的 MADRL 进行多 UAV 协同机动决策以实现追击-规避","authors":"Delin Luo , Zihao Fan , Ziyi Yang , Yang Xu","doi":"10.1016/j.dt.2023.11.013","DOIUrl":null,"url":null,"abstract":"<div><p>Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning (MADRL) is proposed. In this method, an improved CommNet network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit (GRU) is added to the actor-network structure to remember historical environmental states. Subsequently, another GRU is designed as a communication channel in the CommNet core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.</p></div>","PeriodicalId":58209,"journal":{"name":"Defence Technology(防务技术)","volume":"35 ","pages":"Pages 187-197"},"PeriodicalIF":5.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221491472300301X/pdfft?md5=f29cfb1a4d800c0646b4bec364fb1a5e&pid=1-s2.0-S221491472300301X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Multi-UAV cooperative maneuver decision-making for pursuit-evasion using improved MADRL\",\"authors\":\"Delin Luo , Zihao Fan , Ziyi Yang , Yang Xu\",\"doi\":\"10.1016/j.dt.2023.11.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning (MADRL) is proposed. In this method, an improved CommNet network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit (GRU) is added to the actor-network structure to remember historical environmental states. Subsequently, another GRU is designed as a communication channel in the CommNet core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.</p></div>\",\"PeriodicalId\":58209,\"journal\":{\"name\":\"Defence Technology(防务技术)\",\"volume\":\"35 \",\"pages\":\"Pages 187-197\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S221491472300301X/pdfft?md5=f29cfb1a4d800c0646b4bec364fb1a5e&pid=1-s2.0-S221491472300301X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Defence Technology(防务技术)\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221491472300301X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Defence Technology(防务技术)","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221491472300301X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Multi-UAV cooperative maneuver decision-making for pursuit-evasion using improved MADRL
Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning (MADRL) is proposed. In this method, an improved CommNet network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit (GRU) is added to the actor-network structure to remember historical environmental states. Subsequently, another GRU is designed as a communication channel in the CommNet core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.
Defence Technology(防务技术)Mechanical Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering
CiteScore
8.70
自引率
0.00%
发文量
728
审稿时长
25 days
期刊介绍:
Defence Technology, a peer reviewed journal, is published monthly and aims to become the best international academic exchange platform for the research related to defence technology. It publishes original research papers having direct bearing on defence, with a balanced coverage on analytical, experimental, numerical simulation and applied investigations. It covers various disciplines of science, technology and engineering.