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Conference Papers Year : 2010

Best Arm Identification in Multi-Armed Bandits

Abstract

We consider the problem of finding the best arm in a stochastic multi-armed bandit game. The regret of a forecaster is here defined by the gap between the mean reward of the optimal arm and the mean reward of the ultimately chosen arm. We propose a highly exploring UCB policy and a new algorithm based on successive rejects. We show that these algorithms are essentially optimal since their regret decreases exponentially at a rate which is, up to a logarithmic factor, the best possible. However, while the UCB policy needs the tuning of a parameter depending on the unobservable hardness of the task, the successive rejects policy benefits from being parameter-free, and also independent of the scaling of the rewards. As a by-product of our analysis, we show that identifying the best arm (when it is unique) requires a number of samples of order (up to a log(K) factor) Σ i 1/Δ2i, where the sum is on the suboptimal arms andΔi represents the difference between the mean reward of the best arm and the one of arm i. This generalizes the well-known fact that one needs of order of 1/Δ2 samples to differentiate the means of two distributions with gap Δ.
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Dates and versions

hal-00654404 , version 1 (21-12-2011)

Identifiers

  • HAL Id : hal-00654404 , version 1

Cite

Jean-Yves Audibert, Sébastien Bubeck. Best Arm Identification in Multi-Armed Bandits. COLT - 23th Conference on Learning Theory - 2010, Jun 2010, Haifa, Israel. 13 p. ⟨hal-00654404⟩
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