A Tutorial on Thompson Sampling Article Swipe

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Daniel Russo , Benjamin Van Roy , Abbas Kazerouni , Ian Osband , Zheng Wen ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1707.02038

Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of problems in a computationally efficient manner and is therefore enjoying wide use. This tutorial covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and reinforcement learning in Markov decision processes. Most of these problems involve complex information structures, where information revealed by taking an action informs beliefs about other actions. We will also discuss when and why Thompson sampling is or is not effective and relations to alternative algorithms.

Related Topics

Computer Science

Reinforcement Learning

Artificial Intelligence

Range (Aeronautics)

Machine Learning

Theoretical Computer Science

Concepts

Computer science Reinforcement learning Markov decision process Artificial intelligence Range (aeronautics) Thompson sampling Sampling (signal processing) Shortest path problem Machine learning Product (mathematics) Theoretical computer science Mathematical optimization Markov process Bayesian probability Mathematics Geometry Statistics Filter (signal processing) Composite material Graph Materials science Computer vision

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1707.02038
PDF: https://arxiv.org/pdf/1707.02038
OA Status: green
Cited By: 39
References: 71
Related Works: 10
OpenAlex ID: https://openalex.org/W2731829640

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DOI: https://doi.org/10.48550/arxiv.1707.02038

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Title: A Tutorial on Thompson Sampling

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Type: preprint

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Language: en

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Publication year: 2017

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Publication date: 2017-07-07

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Authors: Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen

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Landing page: https://arxiv.org/abs/1707.02038

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PDF URL: https://arxiv.org/pdf/1707.02038

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Concepts: Computer science, Reinforcement learning, Markov decision process, Artificial intelligence, Range (aeronautics), Thompson sampling, Sampling (signal processing), Shortest path problem, Machine learning, Product (mathematics), Theoretical computer science, Mathematical optimization, Markov process, Bayesian probability, Mathematics, Geometry, Statistics, Filter (signal processing), Composite material, Graph, Materials science, Computer vision

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Cited by: 39

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References (count): 71

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