2008/10/26 by Keqin Liu, Qing Zhao, Liu, Keqin +1 · 14 citations
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Advanced Wireless Network Optimization #Cognitive Radio Networks and Spectrum Sensing #FOS: Computer and information sciences #Information Theory (cs.IT)
paper · pdf · doi:10.48550/arxiv.0810.4658
openalex publication_date 2008/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider a class of restless multi-armed bandit problems (RMBP) that arises in dynamic multichannel access, user/server scheduling, and optimal activation in multi-agent systems. For this class of RMBP, we establish the indexability and obtain Whittle's index in closed-form for both discounted and average reward criteria. These results lead to a direct implementation of Whittle's index policy with remarkably low complexity. When these Markov chains are stochastically identical, we show that Whittle's index policy is optimal under certain conditions. Furthermore, it has a semi-universal structure that obviates the need to know the Markov transition probabilities. The optimality and the semi-universal structure result from the equivalency between Whittle's index policy and the myopic policy established in this work. For non-identical channels, we develop efficient algorithms for computing a performance upper bound given by Lagrangian relaxation. The tightness of the upper bound and the near-optimal performance of Whittle's index policy are illustrated with simulation examples.