Etienne Boursier (ENS Paris-Saclay)

We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider the challenging heterogeneous setting, in which different arms may have different means for different players, and propose a new, efficient algorithm that combines the idea of leveraging forced collisions for implicit communication and that of performing matching eliminations. We give a finite-time analysis of our algorithm, bounding its regret by O((log T)^{1+kappa}) for any fixed kappa>0. If the optimal assignment of players to arms is unique, we further show that it attains the optimal O(log(T)) regret, solving an open question raised at NeurIPS 2018.

AISTATS 2020, The 23rd International Conference on Artificial Intelligence and Statistics, June 3 – 5, 2020 – Palermo, Sicily, Italy