An Optimal Elimination Algorithm for Learning a Best Arm

NeurIPS 2020

An Optimal Elimination Algorithm for Learning a Best Arm

Dec 06, 2020
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We consider the classic problem of $(\epsilon,\delta)$-PAC learning a best arm where the goal is to identify with confidence $1-\delta$ an arm whose mean is an $\epsilon$-approximation to that of the highest mean arm in a multi-armed bandit setting. This problem is one of the most fundamental problems in statistics and learning theory, yet somewhat surprisingly its worst-case sample complexity is not well understood. In this paper, we propose a new approach for $(\epsilon,\delta)$-PAC learning a best arm. This approach leads to an algorithm whose sample complexity converges to \emph{exactly} the optimal sample complexity of $(\epsilon,\delta)$-learning the mean of $n$ arms separately and we complement this result with a conditional matching lower bound. More specifically: Speakers: Avinatan Hassidim, Ron Kupfer, Yaron Singer

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