Paper Detail
Merve Karakas, Osama Hanna, Lin F. Yang, Christina Fragouli
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.
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@article{karakas2026best,
title = {Best-Arm Identification with Noisy Actuation},
author = {Merve Karakas and Osama Hanna and Lin F. Yang and Christina Fragouli},
year = {2026},
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.},
url = {https://arxiv.org/abs/2604.02255},
keywords = {cs.IT, cs.LG},
eprint = {2604.02255},
archiveprefix = {arXiv},
}
{}