When learning systems communicate over noisy channels, the fundamental limits of error-free communication directly determine how efficiently you can identify the best option in a bandit problem.
This paper tackles a multi-armed bandit problem where a learner must identify the best option (arm) but can only communicate with an agent through a noisy channel. The researchers develop communication strategies that connect to information theory concepts, showing how channel quality affects the ability to find the best arm.