Multi-armed Bandit, Dynamic Environments and Meta-Bandits

Cédric Hartland · Sylvain Gelly · Nicolas Baskiotis · Olivier Teytaud · Michèle Sébag

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

Abstract

This paper presents the Adapt-EvE algorithm, extending the UCBT online learning algorithm (Auer et al. 2002) to abruptly changing environments. Adapt-EvE features an adaptive change-point detection test based on Page-Hinkley statistics, and two alternative xtra-exploration procedures respectively based on smooth-restart and Meta-Bandits.

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References

2002Finite-time Analysis of the Multiarmed Bandit Problem 1954CONTINUOUS INSPECTION SCHEMES 2006Prediction, Learning, and Games 1971Procedures for Reacting to a Change in Distribution 1970Inference About the Change-Point in a Sequence of Random Variables 2004Detecting Change in Data Streams

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2016Multi-armed bandit problem with known trend 2018Learning Contextual Bandits in a Non-stationary Environment 2020Sliding-Window Thompson Sampling for Non-Stationary Settings 2017The non-stationary stochastic multi-armed bandit problem 2017Taming Non-stationary Bandits: A Bayesian Approach 2011Unimodal Bandits

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