Efficient Decentralized Multi-agent Learning in Asymmetric Bipartite Queueing Systems

• 成果类型: Article
• 署名作者: Freund, Daniel; Lykouris, Thodoris; Weng, Wentao
• 署名单位: Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0291
• 发表日期: 2024
• 页码: 1049-1070
• 关键词: Service Systems; multiarmed bandits; decentralization

摘要

We study decentralized multiagent learning in bipartite queueing systems, a standard model for service systems. In particular, N agents request service from K servers in a fully decentralized way, that is, by running the same algorithm without communication. Previous decentralized algorithms are restricted to symmetric systems, have performance that is degrading exponentially in the number of servers, require communication through shared randomness and unique agent identities, and are computationally demanding. In contrast, we provide a simple learning algorithm that, when run decentrally by each agent, leads the queueing system to have efficient performance in general asymmetric bipartite queueing systems while also having additional robustness properties. Along the way, we provide the first provably efficient upper confidence bound-based algorithm for the centralized case of the problem.