NIPS Proceedingsβ

When are Kalman-Filter Restless Bandits Indexable?

Part of: Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Authors

Conference Event Type: Poster

Abstract

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 {\it indexable} in the sense that the {\it 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 {\it Schur-convexity} and {\it mechanical words}, which are particularbinary strings intimately related to {\it palindromes}.