Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits

TL;DR

This paper explores how queue sampling strategies affect multi-armed bandit algorithms in remote control scenarios with unreliable communication, proposing a new stochastic method to improve energy efficiency and performance.

Contribution

It introduces a novel stochastic queue sampling approach and evaluates its impact on MAB algorithms under latency and reliability constraints.

Findings

The new approach improves regret performance over existing methods.

Queue sampling strategies significantly influence MAB effectiveness.

Trade-offs between reward maximization and energy consumption are characterized.

Abstract

In recent years, the integration of communication and control systems has gained significant traction in various domains, ranging from autonomous vehicles to industrial automation and beyond. Multi-armed bandit (MAB) algorithms have proven their effectiveness as a robust framework for solving control problems. In this work, we investigate the use of MAB algorithms to control remote devices, which faces considerable challenges primarily represented by latency and reliability. We analyze the effectiveness of MABs operating in environments where the action feedback from controlled devices is transmitted over an unreliable communication channel and stored in a Geo/Geo/1 queue. We investigate the impact of queue sampling strategies on the MAB performance, and introduce a new stochastic approach. Its performance in terms of regret is evaluated against established algorithms in the literature for both upper confidence bound (UCB) and Thompson Sampling (TS) algorithms. Additionally, we study the trade-off between maximizing rewards and minimizing energy consumption.