mlsanityData to 7 Oct 2026

Best-Arm Identification with Noisy Actuation

arXiv 2604.02255 ↗First posted 2 Apr 2026cs.IT cs.LG math.ITcited by 0

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Abstract (arXiv)

In this paper, we consider a multi-armed bandit (MAB) instance and study how to identify the best arm when arm commands are conveyed from a central learner to a distributed agent over a discrete memoryless channel (DMC). Depending on the agent capabilities, we provide communication schemes along with their analysis, which interestingly relate to the zero-error capacity of the underlying DMC.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 2 Apr 2026.

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References · 7

  1. 2014On the Complexity of Best Arm Identification in Multi-Armed Bandit Modelscited by 280
  2. 2016Optimal Best Arm Identification with Fixed Confidencecited by 233
  3. 2013lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Banditscited by 148
  4. 2018Stochastic bandits robust to adversarial corruptionscited by 118
  5. 2019Better Algorithms for Stochastic Bandits with Adversarial Corruptionscited by 91
  6. 2020Prediction with Corrupted Expert Advicecited by 26
  7. 2023Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channelscited by 2

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