Bandit problems with infinitely many arms

Abstract

We consider a bandit problem consisting of a sequence of n choices from an infinite number of Bernoulli arms, with n → ∝. The objective is to minimize the long-run failure rate. The Bernoulli parameters are independent observations from a distribution F. We first assume F to be the uniform distribution on (0, 1) and consider various extensions. In the uniform case we show that the best lower bound for the expected failure proportion is between √2/√n and 2/√n and we exhibit classes of strategies that achieve the latter.