Best-Arm Identification with Noisy Actuation
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...
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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Apr 3, 2026
Data Science
Machine Learning
0