When are Kalman-filter restless bandits indexable?
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Abstract (arXiv)
We study the restless bandit associated with an extremely simple scalar Kalman filter model in discrete time. Under certain assumptions, we prove that the problem is indexable in the sense that the Whittle index is a non-decreasing function of the relevant belief state. In spite of the long history of this problem, this appears to be the first such proof. We use results about Schur-convexity and mechanical words, which are particular binary strings intimately related to palindromes.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2021NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RLcited by 27
- 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernelscited by 1
- 2024An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Trackingcited by 0
- 2026From Relaxed Indexability to Exact Indexability: A t-Step Approach for Partially Observable Restless Banditscited by 0
- 2026From Relaxed Indexability to Exact Indexability: A t-Step Approach for Partially Observable Restless Bandits25 Aug 2026
- 2025Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels22 Jun 2025
- 2024An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Tracking19 Feb 2024
- 2021NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL5 Oct 2021