Multi-Agent Reinforcement Learning for a Multichannel Uplink Random Access: Congestion Game Perspective
- Authors
- Zhao, Yu; Lee, Joohyun; Seo, Jun-Bae
- Issue Date
- Nov-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- congestion game; Multi-armed bandit; nash equilibrium; random access; reinforcement learning
- Citation
- Proceedings of 2023 12th International Conference on Awareness Science and Technology, iCAST 2023, pp 156 - 160
- Pages
- 5
- Indexed
- SCOPUS
- Journal Title
- Proceedings of 2023 12th International Conference on Awareness Science and Technology, iCAST 2023
- Start Page
- 156
- End Page
- 160
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/69497
- DOI
- 10.1109/iCAST57874.2023.10359301
- ISSN
- 0000-0000
- 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. © 2023 IEEE.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 해양과학대학 > 지능형통신공학과 > Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.gnu.ac.kr/handle/sw.gnu/69497)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.