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AI researchers resolve best-arm identification problem using Lean 4

By PulseAugur Editorial · [1 sources] ·

Researchers have resolved conjectures regarding the instance-wise sample complexity of the best-arm identification problem. They established a lower bound related to gap entropy and introduced a single algorithm that achieves near-optimal sample complexity. The proofs for these main theorems have been formalized using the Lean 4 programming language. AI

IMPACT This research advances theoretical understanding in reinforcement learning, potentially leading to more efficient AI algorithms for decision-making tasks.

RANK_REASON Academic paper detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI researchers resolve best-arm identification problem using Lean 4

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Academic paper detailing theoretical computer science research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiarui Yao, Jiaxi Zhao, Xiangxin Zhou ·

    Gap Entropy and Almost Instance-Wise Optimal Best-Arm Identification

    arXiv:2609.13703v1 Announce Type: cross Abstract: In the best-arm identification problem, we are given $n$ stochastic arms with unknown means and wish to identify the arm with the largest mean with probability at least $1-\delta$, using as few samples as possible. We consider ind…

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