Communication Dans Un Congrès Année : 2017

Memory Bandits: a Bayesian approach for the Switching Bandit Problem

Réda Alami
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Résumé

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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Dates et versions

hal-01811697 , version 1 (13-06-2018)

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  • HAL Id : hal-01811697 , version 1

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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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