Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits

Abstract

We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subset of arms toward the optimal distribution. We show that our policies are asymptotically optimal with an $O(1/\sqrt{N})$ optimality gap for an $N$-armed problem, assuming only a unichain and aperiodicity assumption. Our approach departs from most existing work that focuses on index or priority policies, which rely on the Global Attractor Property (GAP) to guarantee convergence to the optimum, or a recently developed simulation-based policy, which requires a Synchronization Assumption (SA).

68 pages, 17 figures

References in corpus (4)

• Asymptotically optimal priority policies for indexable and nonindexable restless bandits
• Markovian restless bandits and index policies: A review
• LP-based policies for restless bandits: necessary and sufficient conditions for (exponentially fast) asymptotic optimality
• Testing Indexability and Computing Whittle and Gittins Index in Subcubic Time