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Memory Bandits: a Bayesian approach for the Switching Bandit Problem

Réda Alami 1 Odalric Maillard 2 Raphael Féraud 1
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
Abstract : The Thompson Sampling exhibits excellent results in practice and it has been shown to be asymptotically optimal. The extension of Thompson Sampling algorithm to the Switching Multi-Armed Bandit problem, proposed in [13], is a Thompson Sampling equiped with a Bayesian online change point detector [1]. In this paper, we propose another extension of this approach based on a Bayesian aggregation framework. Experiments provide some evidences that in practice, the proposed algorithm compares favorably with the previous version of Thompson Sampling for the Switching Multi-Armed Bandit Problem, while it outperforms clearly other algorithms of the state-of-the-art.
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Submitted on : Wednesday, June 13, 2018 - 1:02:06 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:21 PM
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  • HAL Id : hal-01811697, version 1



Réda Alami, Odalric Maillard, Raphael Féraud. Memory Bandits: a Bayesian approach for the Switching Bandit Problem. NIPS 2017 - 31st Conference on Neural Information Processing Systems, Dec 2017, Long Beach, United States. ⟨hal-01811697⟩



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