Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems
This addresses a problem in service systems like ride-sharing or cloud computing, offering a robust solution for decentralized coordination, though it builds incrementally on existing UCB-based approaches.
The authors tackled decentralized multi-agent learning in asymmetric bipartite queueing systems, developing a simple algorithm that achieves efficient performance without communication, unlike prior methods limited to symmetric systems with exponential degradation.
We study decentralized multi-agent 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, 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 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 UCB-based algorithm for the centralized case of the problem.