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Conference Papers Year : 2008

Infinitely many-armed bandits

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Abstract

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 matches (up to a logarithmic factor) the upper-bound in some cases.
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Dates and versions

hal-00830178 , version 1 (04-06-2013)

Identifiers

  • HAL Id : hal-00830178 , version 1

Cite

Yizao Wang, Jean-Yves Audibert, Rémi Munos. Infinitely many-armed bandits. Advances in Neural Information Processing Systems, 2008, Canada. ⟨hal-00830178⟩
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