Bounded Regret for Finite-Armed Structured Bandits

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Authors

Tor Lattimore, Remi Munos

Abstract

<p>We study a new type of K-armed bandit problem where the expected return of one arm may depend on the returns of other arms. We present a new algorithm for this general class of problems and show that under certain circumstances it is possible to achieve finite expected cumulative regret. We also give problem-dependent lower bounds on the cumulative regret showing that at least in special cases the new algorithm is nearly optimal.</p>