EVaR-Optimal Arm Identification in Bandits

Mehrasa Ahmadipour; Aurélien Garivier

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arXiv · 2510.04728

EVaR-Optimal Arm Identification in Bandits

Mehrasa Ahmadipour·Aurélien Garivier

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Abstract

We study the fixed-confidence best arm identification (BAI) problem within the multi-armed bandit (MAB) framework under the Entropic Value-at-Risk (EVaR) criterion. Our analysis considers a nonparametric setting, allowing for general reward distributions bounded in [0,1]. This formulation addresses the critical need for risk-averse decision-making in high-stakes environments, such as finance, moving beyond simple expected value optimization. We propose a $δ$-correct, Track-and-Stop based algorithm and derive a corresponding lower bound on the expected sample complexity, which we prove is asymptotically matched. The implementation of our algorithm and the characterization of the lower bound both require solving a complex convex optimization problem and a related, simpler non-convex one.

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BibTeX RIS

Mehrasa Ahmadipour, Aurélien Garivier. 2025-10-06. EVaR-Optimal Arm Identification in Bandits. https://arxiv.org/abs/2510.04728

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