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arXiv · 2306.09048

Optimal Best-Arm Identification in Bandits with Access to Offline Data

Shubhada Agrawal·Sandeep Juneja·Karthikeyan Shanmugam·Arun Sai Suggala
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Abstract

Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature. In this paper, we consider combining offline data with online learning, an area less studied but of obvious practical importance. We consider the stochastic $K$-armed bandit problem, where our goal is to identify the arm with the highest mean in the presence of relevant offline data, with confidence $1-\delta$. We conduct a lower bound analysis on policies that provide such $1-\delta$ probabilistic correctness guarantees. We develop algorithms that match the lower bound on sample complexity when $\delta$ is small. Our algorithms are computationally efficient with an average per-sample acquisition cost of $\tilde{O}(K)$, and rely on a careful characterization of the optimality conditions of the lower bound problem.

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Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam, Arun Sai Suggala. 2023-06-15. Optimal Best-Arm Identification in Bandits with Access to Offline Data. https://arxiv.org/abs/2306.09048

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