Anytime optimal algorithms in stochastic multi-armed bandits

Rémy Degenne · Vianney Perchet

29 citations0/0 files storedInternational Conference on Machine Learning

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

Abstract

We introduce an anytime algorithm for stochastic multi-armed bandit with optimal distribution free and distribution dependent bounds (for a specific family of parameters). The performances of this algorithm (as well as another one motivated by the conjectured optimal bound) are evaluated empirically. A similar analysis is provided with full information, to serve as a benchmark.

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References

2002Finite-time Analysis of the Multiarmed Bandit Problem 2006Prediction, Learning, and Games 1952Some aspects of the sequential design of experiments 2002Gambling in a rigged casino: The adversarial multi-armed bandit problem 2010Best Arm Identification in Multi-Armed Bandits 2012Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis 2012Further Optimal Regret Bounds for Thompson Sampling 2004The Sample Complexity of Exploration in the Multi-Armed Bandit Problem

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