One for All and All for One: Distributed Learning of Fair Allocations With Multi-Player Bandits

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, represented as an $N\times M$ 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 in that tur...