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arXiv · 1205.4217

Thompson Sampling: An Asymptotically Optimal Finite Time Analysis

Emilie Kaufmann·Nathaniel Korda·Rémi Munos
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Abstract

The question of the optimality of Thompson Sampling for solving the stochastic multi-armed bandit problem had been open since 1933. In this paper we answer it positively for the case of Bernoulli rewards by providing the first finite-time analysis that matches the asymptotic rate given in the Lai and Robbins lower bound for the cumulative regret. The proof is accompanied by a numerical comparison with other optimal policies, experiments that have been lacking in the literature until now for the Bernoulli case.

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Emilie Kaufmann, Nathaniel Korda, Rémi Munos. 2012-07-19. Thompson Sampling: An Asymptotically Optimal Finite Time Analysis. https://arxiv.org/abs/1205.4217

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