Batched Bandit Problems

Abstract

Motivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic bandits under the constraint that the employed policy must split trials into a small number of batches. Our results show that a very small number of batches gives close to minimax optimal regret bounds. As a byproduct, we derive optimal policies with low switching cost for stochastic bandits.National Science Foundation (U.S.) (Grant DMS-1317308)National Science Foundation (U.S.) (CAREER-DMS-1053987)Meimaris Famil