论文标题
在不对称两部分排队系统中有效分散的多机构学习
Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems
论文作者
论文摘要
我们研究了两分排队系统中的分散多机构学习，这是服务系统的标准模型。特别是，N代理以完全分散的方式向K服务器请求服务，即通过在没有通信的情况下运行相同的算法。以前的分散算法仅限于对称系统，其性能在服务器数量中呈指数降低，需要通过共享的随机性和唯一的代理身份进行通信，并且在计算上要求。相比之下，我们提供了一种简单的学习算法，当每个代理商分散时，导致排队系统在一般不对称的两部分排队系统中具有有效的性能，同时还具有额外的鲁棒性属性。在此过程中，我们为问题的集中式案例提供了第一个可证明有效的基于UCB的算法。
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.
