Analyzing and enhancing sueue sampling for energy-efficient remote control of bandits - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Analyzing and enhancing sueue sampling for energy-efficient remote control of bandits

Résumé

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.

Dates et versions

hal-04583346 , version 1 (22-05-2024)

Identifiants

Citer

Hiba Dakdouk, Mohamed Sana, Mattia Merluzzi. Analyzing and enhancing sueue sampling for energy-efficient remote control of bandits. MeditCom 2024 - 2024 IEEE International Mediterranean Conference on Communications and Networking Madrid, Spain, 2024, pp. 239-244, Jul 2024, Madrid, Spain. pp.239-244, ⟨10.1109/MeditCom61057.2024.10621192⟩. ⟨hal-04583346⟩
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