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.
Fields of Study & Subjects
Advanced Bandit Algorithms Research
Reinforcement Learning in Robotics
Machine Learning and Algorithms
Publication details
- Journal / source
- 2026 IEEE International Symposium on Information Theory (ISIT)
- Publisher
- IEEE
- Pages
- 1–6
- Publication date
- 1 June 2026