Efficient Decentralized Multi-agent Learning in Asymmetric Bipartite Queueing Systems
Daniel Freund, Thodoris Lykouris, Wentao Weng
Operations Research · 2024 · Volume 72 · Issue 3
Abstract
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. Funding: T. Lykouris would like to acknowledge funding from Google Research. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.0291 .
Keywords
Machine Learning and Data Science, service systems, multiarmed bandits, decentralization
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