A verification theorem for threshold-indexability of real-state discounted restless bandits
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Abstract (arXiv)
The Whittle index, which characterizes optimal policies for controlling certain single restless bandit projects (a Markov decision process with two actions: active and passive) is the basis for a widely used heuristic index policy for the intractable restless multiarmed bandit problem. Yet two roadblocks need to be overcome to apply such a policy: the individual projects in the model at hand must be shown to be indexable, so that they possess a Whittle index; and the index must be evaluated. Such roadblocks can be especially vexing when project state spaces are real intervals, as in recent sensor scheduling applications. This paper presents sufficient conditions for indexability (relative to a generalized Whittle index) of general real-state discrete-time restless bandits under the discounted criterion, which are not based on elucidating properties of the optimal value function and do not require proving beforehand optimality of threshold policies as in prevailing approaches. The main contribution is a verification theorem establishing that, if project performance metrics under threshold policies and an explicitly defined marginal productivity (MP) index satisfy three conditions, then the project is indexable with its generalized Whittle index being given by the MP index, and threshold policies are optimal for dynamic project control.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 1.
5 papers.
- 2021Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Settingcited by 12
- 2024Tabular and Deep Learning for the Whittle Indexcited by 3
- 2023General Formulation and PCL-Analysis for Restless Bandits with Limited Observabilitycited by 1
- 2024An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Trackingcited by 0
- 2025MARBLE: Multi-Armed Restless Bandits in Latent Markovian Environmentcited by 0
- 2025MARBLE: Multi-Armed Restless Bandits in Latent Markovian Environment12 Nov 2025
- 2024Tabular and Deep Learning for the Whittle Index4 Jun 2024
- 2024An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Tracking19 Feb 2024
- 2023General Formulation and PCL-Analysis for Restless Bandits with Limited Observability6 Jul 2023
- 2021Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting14 Jun 2021