Bounded Regret for Finite-Armed Structured Bandits

Tor Lattimore · Rémi Munos

80 citations0/0 files storedarXiv (Cornell University)

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Advanced Bandit Algorithms Research Reinforcement Learning in Robotics Optimization and Search Problems

Abstract

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.

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References

2002Finite-time Analysis of the Multiarmed Bandit Problem 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems 2011Analysis of Thompson Sampling for the multi-armed bandit problem 2012Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis 2012Further Optimal Regret Bounds for Thompson Sampling 2010UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem 2008Online Optimization in X-Armed Bandits 2009A Structured Multiarmed Bandit Problem and the Greedy Policy

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2021Multi-Armed Bandits With Correlated Arms

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