Algorithms for Infinitely Many-Armed Bandits

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Authors

Yizao Wang, Jean-yves Audibert, Rémi Munos

Abstract

<p>We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new selected arm which characterizes its probability of being a near-optimal arm. Our assumption is weaker than in previous works. We describe algorithms based on upper-confidence-bounds applied to a restricted set of randomly selected arms and provide upper-bounds on the resulting expected regret. We also derive a lower-bound which matchs (up to logarithmic factors) the upper-bound in some cases.</p>