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Conference Papers Year : 2016

Optimal Best Arm Identification with Fixed Confidence

Abstract

We give a complete characterization of the complexity of best-arm identification in one-parameter bandit problems. We prove a new, tight lower bound on the sample complexity. We propose the `Track-and-Stop' strategy, which we prove to be asymptotically optimal. It consists in a new sampling rule (which tracks the optimal proportions of arm draws highlighted by the lower bound) and in a stopping rule named after Chernoff, for which we give a new analysis.
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Dates and versions

hal-01273838 , version 1 (14-02-2016)
hal-01273838 , version 2 (01-06-2016)

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Aurélien Garivier, Emilie Kaufmann. Optimal Best Arm Identification with Fixed Confidence. 29th Annual Conference on Learning Theory (COLT), Jun 2016, New York, United States. ⟨hal-01273838v2⟩
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