My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits

Autor:	Bistritz, Ilai, Baharav, Tavor Z., Leshem, Amir, Bambos, Nicholas
Rok vydání:	2020
Předmět:	Computer Science - Computer Science and Game Theory Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	Consider N cooperative but non-communicating players where each plays one out of M arms for T turns. Players have different utilities for each arm, representable as an NxM matrix. These utilities are unknown to the players. In each turn players select an arm and receive a noisy observation of their utility for it. However, if any other players selected the same arm that turn, all colliding players will all receive zero utility due to the conflict. No other communication or coordination between the players is possible. Our goal is to design a distributed algorithm that learns the matching between players and arms that achieves max-min fairness while minimizing the regret. We present an algorithm and prove that it is regret optimal up to a $\log\log T$ factor. This is the first max-min fairness multi-player bandit algorithm with (near) order optimal regret. Comment: ICML 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.09808 Zobrazit plný text záznamu View this record from Arxiv