Optimal Best Arm Identification with Fixed Confidence

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Aurélien Garivier, Emilie Kaufmann ;
29th Annual Conference on Learning Theory, PMLR 49:998-1027, 2016.

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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