Representative Action Selection for Large Action-Space Meta-Bandits

• URL: http://arxiv.org/abs/2505.18269v1
• Date: Fri, 23 May 2025 18:08:57 GMT
• Title: Representative Action Selection for Large Action-Space Meta-Bandits
• Authors: Quan Zhou, Mark Kozdoba, Shie Mannor,
• Abstract summary: We study the problem of selecting a subset from a large action space shared by a family of bandits.<n>We assume that similar actions tend to have related payoffs, modeled by a Gaussian process.<n>We propose a simple epsilon-net algorithm to select a representative subset.
• Score: 49.386906771833274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of selecting a subset from a large action space shared by a family of bandits, with the goal of achieving performance nearly matching that of using the full action space. We assume that similar actions tend to have related payoffs, modeled by a Gaussian process. To exploit this structure, we propose a simple epsilon-net algorithm to select a representative subset. We provide theoretical guarantees for its performance and compare it empirically to Thompson Sampling and Upper Confidence Bound.

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