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Quantum Multi - Agent Reinforcement Learning Software Design and Visual Simulations for Multi - Drone Mobility Control
  • Park, Soohyun ;
  • Kim, Gyu Seon ;
  • Jung, Soyi ;
  • Kim, Joongheon
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dc.contributor.authorPark, Soohyun-
dc.contributor.authorKim, Gyu Seon-
dc.contributor.authorJung, Soyi-
dc.contributor.authorKim, Joongheon-
dc.date.issued2024-01-01-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/37119-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206079106&origin=inward-
dc.description.abstractQuantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) features non-stationarity and uncertain properties. Therefore, this paper presents a simulation software framework for novel QMARL to control autonomous multi-drone mobility, i.e., quantum multi-drone reinforcement learning. Our proposed framework ac-complishes reasonable reward convergence and service quality performance with fewer trainable parameters. Furthermore, it shows more stable training results. Lastly, our proposed software allows us to analyze the training process and results.-
dc.description.sponsorshipThis research was funded by National Research Foundation of Korea (NRF) (2022R1A2C2004869) and also by NRF grant funded by the Korea government (MSIT) (2022R1C1C1010766).-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.subject.meshDesign simulations-
dc.subject.meshLearning software-
dc.subject.meshMachine-learning-
dc.subject.meshMobility control-
dc.subject.meshMulti-agent reinforcement learning-
dc.subject.meshQuantum machine learning-
dc.subject.meshQuantum machines-
dc.subject.meshReinforce-ment learning-
dc.subject.meshSimulation-
dc.subject.meshVisual simulation-
dc.titleQuantum Multi - Agent Reinforcement Learning Software Design and Visual Simulations for Multi - Drone Mobility Control-
dc.typeConference-
dc.citation.conferenceDate2024.8.21. ~ 2024.8.23.-
dc.citation.conferenceName2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024-
dc.citation.edition2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024-
dc.citation.title2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024-
dc.identifier.bibliographicCitation2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024-
dc.identifier.doi10.1109/apwcs61586.2024.10679327-
dc.identifier.scopusid2-s2.0-85206079106-
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10679110-
dc.subject.keywordDrone-
dc.subject.keywordQuantum Machine Learning-
dc.subject.keywordReinforce-ment Learning-
dc.subject.keywordSimulations-
dc.subject.keywordVisualization-
dc.type.otherConference Paper-
dc.description.isoafalse-
dc.subject.subareaComputer Networks and Communications-
dc.subject.subareaSafety, Risk, Reliability and Quality-
dc.subject.subareaControl and Optimization-
dc.subject.subareaInstrumentation-
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