统治或删除：系列独裁中的分散竞争土匪
Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship
摘要：
双边匹配市场中的在线学习，需求方代理不断竞争与供应方（手臂）匹配，在匹配平台（例如UpWork，TaskRabbit）上的部分信息下抽象复杂的交互。我们研究了分散的串行独裁设置，双边匹配市场的需求方代理商有未知的和异质的估值超过供应方（武器），而武器有已知的统一偏好的需求方（代理商）。我们设计了第一个分散的算法- UCB与分散的主导臂删除（UCB-D3），代理，不需要任何知识的奖励差距或时间范围。UCB-D3分阶段工作，在每个阶段中，代理删除\emph{dominated arms} -高级代理首选的武器，并根据UCB仅从非劣势武器进行游戏。在该阶段结束时，代理以分散的方式广播，通过{ \em纯开发}估计他们的首选武器。我们证明了这两个，一个新的遗憾下界的分散串行独裁模型，UCB-D3是顺序最优的。
英文摘要：
Online learning in a two-sided matching market, with demand side agents continuously competing to be matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms (e.g. UpWork, TaskRabbit). We study the decentralized serial dictatorship setting, a two-sided matching market where the demand side agents have unknown and heterogeneous valuation over the supply side (arms), while the arms have known uniform preference over the demand side (agents). We design the first decentralized algorithm -- UCB with Decentralized Dominant-arm Deletion (UCB-D3), for the agents, that does not require any knowledge of reward gaps or time horizon. UCB-D3 works in phases, where in each phase, agents delete \emph{dominated arms} -- the arms preferred by higher ranked agents, and play only from the non-dominated arms according to the UCB. At the end of the phase, agents broadcast in a decentralized fashion, their estimated preferred arms through {\em pure exploitation}. We prove both, a new regret lower bound for the decentralized serial dictatorship model, and that UCB-D3 is order optimal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
