Citation Export
DC Field | Value | Language |
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dc.contributor.author | Park, Soohyun | - |
dc.contributor.author | Kim, Gyu Seon | - |
dc.contributor.author | Jung, Soyi | - |
dc.contributor.author | Kim, Joongheon | - |
dc.date.issued | 2024-01-01 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/37119 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206079106&origin=inward | - |
dc.description.abstract | Quantum 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.sponsorship | This 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.iso | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.subject.mesh | Design simulations | - |
dc.subject.mesh | Learning software | - |
dc.subject.mesh | Machine-learning | - |
dc.subject.mesh | Mobility control | - |
dc.subject.mesh | Multi-agent reinforcement learning | - |
dc.subject.mesh | Quantum machine learning | - |
dc.subject.mesh | Quantum machines | - |
dc.subject.mesh | Reinforce-ment learning | - |
dc.subject.mesh | Simulation | - |
dc.subject.mesh | Visual simulation | - |
dc.title | Quantum Multi - Agent Reinforcement Learning Software Design and Visual Simulations for Multi - Drone Mobility Control | - |
dc.type | Conference | - |
dc.citation.conferenceDate | 2024.8.21. ~ 2024.8.23. | - |
dc.citation.conferenceName | 2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024 | - |
dc.citation.edition | 2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024 | - |
dc.citation.title | 2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024 | - |
dc.identifier.bibliographicCitation | 2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024 | - |
dc.identifier.doi | 10.1109/apwcs61586.2024.10679327 | - |
dc.identifier.scopusid | 2-s2.0-85206079106 | - |
dc.identifier.url | http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10679110 | - |
dc.subject.keyword | Drone | - |
dc.subject.keyword | Quantum Machine Learning | - |
dc.subject.keyword | Reinforce-ment Learning | - |
dc.subject.keyword | Simulations | - |
dc.subject.keyword | Visualization | - |
dc.type.other | Conference Paper | - |
dc.description.isoa | false | - |
dc.subject.subarea | Computer Networks and Communications | - |
dc.subject.subarea | Safety, Risk, Reliability and Quality | - |
dc.subject.subarea | Control and Optimization | - |
dc.subject.subarea | Instrumentation | - |
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