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.

翻译：我们研究了二分排队系统中的去中心化多智能体学习问题，这是服务系统的一个标准模型。具体而言，N个智能体以完全去中心化的方式（即运行相同算法且无需通信）向K个服务器请求服务。以往的去中心化算法仅限于对称系统，其性能随服务器数量呈指数级下降，需要借助共享随机性和唯一智能体标识进行通信，且计算需求较高。相比之下，我们提出了一种简单的学习算法，当每个智能体以去中心化方式运行时，能在一般非对称二分排队系统中实现高效性能，同时具备额外的鲁棒性。在此过程中，我们首次为问题的集中式情形提供了具有可证明高效性的基于UCB的算法。