Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Multi-Agent Reinforcement Learning for a Random Access Game

Authors
Lee, DongwooZhao, YuSeo, Jun-BaeLee, Joohyun
Issue Date
Aug-2022
Publisher
Institute of Electrical and Electronics Engineers
Keywords
Multi-armed bandit; nash equilibrium; non-cooperative game; random access
Citation
IEEE Transactions on Vehicular Technology, v.71, no.8, pp 9119 - 9124
Pages
6
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Vehicular Technology
Volume
71
Number
8
Start Page
9119
End Page
9124
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112726
DOI
10.1109/TVT.2022.3176722
ISSN
0018-9545
1939-9359
Abstract
This work investigates a random access (RA) game for a time-slotted RA system, where N players choose a set of slots of a frame and each frame consists of M multiple time slots. We obtain the pure strategy Nash equilibria (PNEs) of this RA game, where slots are fully utilized as in the centralized scheduling. As an algorithm to realize a PNE (Pure strategy Nash Equilibrium), we propose an Exponential-weight algorithm for Exploration and Exploitation (EXP3)-based multi-agent (MA) learning algorithm, which has the computational complexity of O(N (NmaxT)-T-2). EXP3 is a bandit algorithm designed to find an optimal strategy in a multi-armed bandit (MAB) problem that users do not know the expected payoff of each strategy. Our simulation results show that the proposed algorithm can achieve PNEs. Moreover, it can adapt to time-varying environments, where the number of players varies over time.
Files in This Item
Go to Link
Appears in
Collections
COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Lee, Joo hyun photo

Lee, Joo hyun
ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE