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k-Armed Bandit

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Encyclopedia of Machine Learning and Data Mining

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Recommended Reading

  • Auer P, Cesa-Bianchi N, Freund Y, Schapire RE (2002) The non-stochastic multi-armed bandit problem. SIAM J Comput 32(1):48–77

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  • Berry D, Fristedt B (1985) Bandit problems: sequential allocation of experiments. Chapman and Hall, London/New York

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  • Cesa-Bianchi N, Lugosi G (2006) Prediction, learning, and games. Cambridge University Press, New York

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  • Gittins JC (1989) Multi-armed bandit allocation indices. Wiley, New York

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  • Gittins J, Jones D (1972) A dynamic allocation index for sequential design of experiments. In: Progress in statistics, European meeting of statisticians, Budapest, vol 1, pp 241–266

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  • Lai TL, Robbins H (1985) Asymptotically efficient adaptive allocation rules. Adv Appl Math 6:4–22

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  • Mannor S, Tsitsiklis JN (2004) The sample complexity of exploration in the multi-armed bandit problem. J Mach Learn Res 5:623–648

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  • Robbins H (1952) Some aspects of the sequential design of experiments. Bull Am Math Soc 55:527–535

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Author information

Authors and Affiliations

  1. Israel Institute of Technology, Haifa, Israel

    Shie Mannor

Authors
  1. Shie Mannor
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Mannor, S. (2017). k-Armed Bandit. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_424

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