Efficient Decentralized Multi-Agent Learning in Asymmetric Bipartite Queuing Systems

Abstract

We study decentralized multi-agent learning in bipartite queuing systems, a standard model for service systems. In particular, 𝑁 agents request service from 𝐾 servers in a fully decentralized way, i.e, 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 queuing system to have efficient performance in general asymmetric bipartite queuing systems while also having additional robustness properties. Along the way, we provide the first provably efficient UCB-based algorithm for the centralized case of the problem.