Citation Export
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Park, Chanyoung | - |
| dc.contributor.author | Lee, Haemin | - |
| dc.contributor.author | Yun, Won Joon | - |
| dc.contributor.author | Park, Soohyun | - |
| dc.contributor.author | Jung, Soyi | - |
| dc.contributor.author | Kim, Joongheon | - |
| dc.date.issued | 2023-01-01 | - |
| dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36952 | - |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85175093126&origin=inward | - |
| dc.description.abstract | This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications. | - |
| dc.description.sponsorship | Acknowledgments. This research was funded by National Research Foundation of Korea (2022R1A2C2004869). Chanyoung Park and Haemin Lee equally contributed to this work (first authors). Soohyun Park is a corresponding author (soohyun828@korea.ac.kr). | - |
| dc.language.iso | eng | - |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
| dc.subject.mesh | Access applications | - |
| dc.subject.mesh | Aerial vehicle | - |
| dc.subject.mesh | Autonomous mobilities | - |
| dc.subject.mesh | Mobile access | - |
| dc.subject.mesh | Multi agent | - |
| dc.subject.mesh | Multi agent deep reinforcement learning | - |
| dc.subject.mesh | Non terrestrial network | - |
| dc.subject.mesh | Reinforcement learnings | - |
| dc.subject.mesh | Terrestrial networks | - |
| dc.subject.mesh | Unmanned aerial vehicle | - |
| dc.title | Poster: Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications | - |
| dc.type | Conference | - |
| dc.citation.conferenceDate | 2023.7.18. ~ 2023.7.21. | - |
| dc.citation.conferenceName | 43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023 | - |
| dc.citation.edition | Proceedings - 2023 IEEE 43rd International Conference on Distributed Computing Systems, ICDCS 2023 | - |
| dc.citation.endPage | 1044 | - |
| dc.citation.startPage | 1043 | - |
| dc.citation.title | Proceedings - International Conference on Distributed Computing Systems | - |
| dc.citation.volume | 2023-July | - |
| dc.identifier.bibliographicCitation | Proceedings - International Conference on Distributed Computing Systems, Vol.2023-July, pp.1043-1044 | - |
| dc.identifier.doi | 10.1109/icdcs57875.2023.00126 | - |
| dc.identifier.scopusid | 2-s2.0-85175093126 | - |
| dc.subject.keyword | Autonomous mobility | - |
| dc.subject.keyword | Multi agent deep reinforcement learning (MADRL) | - |
| dc.subject.keyword | Non Terrestrial Network (NTN) | - |
| dc.subject.keyword | Unmanned aerial vehicle (UAV) | - |
| dc.type.other | Conference Paper | - |
| dc.description.isoa | false | - |
| dc.subject.subarea | Software | - |
| dc.subject.subarea | Hardware and Architecture | - |
| dc.subject.subarea | Computer Networks and Communications | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
