Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이주현 | - |
dc.date.accessioned | 2024-06-18T04:23:16Z | - |
dc.date.available | 2024-06-18T04:23:16Z | - |
dc.date.issued | 2023-11-09 | - |
dc.identifier.citation | 2023 12th International Conference on Awareness Science and Technology (iCAST), page. 156-160 | en_US |
dc.identifier.issn | 2325-5994 | en_US |
dc.identifier.issn | 2325-5986 | en_US |
dc.identifier.uri | https://ieeexplore.ieee.org/abstract/document/10359301 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/190795 | - |
dc.description.abstract | In this paper, we propose a time-slotted multichannel uplink random access (RA) game model where players do not cooperate. We first analyze its sum throughput from the congestion game (CG) perspective and obtain the pure strategy Nash equilibria (PNEs) that fully utilize each slot. Then, we propose an Upper Confidence Bound (UCB)-based multi-agent reinforcement learning (MARL) algorithm to realize the PNEs, where UCB is one of the multi-armed bandit algorithms that work by assigning a confidence level for each action. Finally, via simulation, we show that our proposed algorithm can obtain near-optimal average sum throughput in the long run. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No. 2021R1C1C1005126). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA)). This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) under Grant NRF-2021R1F1A1063057. | en_US |
dc.language | en_US | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | ;156-160 | - |
dc.subject | Multi-armed bandit | en_US |
dc.subject | random access | en_US |
dc.subject | reinforcement learning | en_US |
dc.subject | congestion game | en_US |
dc.subject | nash equilibrium | en_US |
dc.title | Multi-Agent Reinforcement Learning for a Multichannel Uplink Random Access: Congestion Game Perspective | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/iCAST57874.2023.10359301 | en_US |
dc.relation.page | 1-5 | - |
dc.contributor.googleauthor | Zhao, Yu | - |
dc.contributor.googleauthor | Lee, Joohyun | - |
dc.contributor.googleauthor | Seo, Jun-Bae | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | SCHOOL OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | joohyunlee | - |
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