Multi-Agent Reinforcement Learning for Wireless User Scheduling: Performance, Scalablility, and Generalization

Kun Yang, Donghao Li, Cong Shen, Jing Yang, Shu Ping Yeh, Jerry Sydir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a multi-agent reinforcement learning (MARL) solution for the user scheduling problem in cellular networks. Incorporating features of this particular use case, we cast the problem in a decentralized partially observable Markov decision process (Dec-POMDP) framework, and present a detailed design of MARL that allows for fully decentralized execution. The performance of MARL against both centralized RL and an engineering heuristic solution is comprehensively evaluated in a system-level simulation. In particular, MARL achieves almost the same total system reward as centralized RL, while enjoying much better scalability with the number of base stations. The transferability of both MARL and centralized RL to new environments is also investigated, and a simple fine-tuning approach based on a general model trained on a pool of environments is shown to have faster convergence while achieving comparable performance with individually trained RL agents, demonstrating its generalization capability.

Original languageEnglish (US)
Title of host publication56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1169-1174
Number of pages6
ISBN (Electronic)9781665459068
DOIs
StatePublished - 2022
Event56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States
Duration: Oct 31 2022Nov 2 2022

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2022-October
ISSN (Print)1058-6393

Conference

Conference56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period10/31/2211/2/22

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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