Thompson Sampling: An Asymptotically Optimal Finite Time Analysis

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Authors Nathaniel Korda, RĂ©mi Munos, Emilie Kaufmann
Journal/Conference Name arXiv preprint
Paper Category
Paper 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.
Date of publication 2012
Code Programming Language MATLAB
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