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ATutorialonThompsonSampling - Stanford University

A Tutorial on Thompson SamplingDaniel J. Russo1, Benjamin Van Roy2, Abbas Kazerouni2, IanOsband3and Zheng Wen41 Columbia University2 Stanford University3 Google DeepMind4 Adobe ResearchABSTRACTT hompson sampling is an algorithm for online decision prob-lems where actions are taken sequentially in a manner thatmust balance between exploiting what is known to maxi-mize immediate performance and investing to accumulatenew information that may improve future performance. Thealgorithm addresses a broad range of problems in a compu-tationally efficient manner and is therefore enjoying wideuse. This tutorial covers the algorithm and its application,illustrating concepts through a range of examples, includingBernoulli bandit problems, shortest path problems, productrecommendation, assortment, active learning with neuralnetworks, and reinforcement learning in Markov decisionprocesses. Most of these problems involve complex informa-tion structures, where information revealed by taking anaction informs beliefs about other actions.

ATutorialonThompsonSampling DanielJ.Russo1, BenjaminVanRoy2, AbbasKazerouni2, Ian Osband3 and ZhengWen4 1ColumbiaUniversity 2StanfordUniversity 3GoogleDeepMind ...

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