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ATutorialonThompsonSampling - Stanford University

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A Tutorial on Thompson SamplingDaniel J. Russo1, Benjamin Van Roy2, Abbas Kazerouni2, IanOsband3and Zheng Wen41Columbia University2Stanford University3Google DeepMind4Adobe ResearchABSTRACTThompson 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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