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Research PaperResearchia:202604.03021

Best-Arm Identification with Noisy Actuation

Merve Karakas

Abstract

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. --- Source: arXiv:2604.02255v1 - http://arxiv.org/abs/2604.02255v1 PDF: https://arxiv.org/pdf/2604.02255v...

Submitted: April 3, 2026Subjects: Machine Learning; Data Science

Description / Details

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.


Source: arXiv:2604.02255v1 - http://arxiv.org/abs/2604.02255v1 PDF: https://arxiv.org/pdf/2604.02255v1 Original Link: http://arxiv.org/abs/2604.02255v1

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Submission Info
Date:
Apr 3, 2026
Topic:
Data Science
Area:
Machine Learning
Comments:
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