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Teoriya Veroyatnostei i ee Primeneniya, 2019, Volume 64, Issue 3, Pages 442–455
DOI: https://doi.org/10.4213/tvp5303 (Mi tvp5303) 		 
This article is cited in 1 scientific paper (total in 1 paper)

Gittins index for simple family of Markov bandit processes with switching cost and no discounting

M. P. Savelov

Novosibirsk State University
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DOI: https://doi.org/10.4213/tvp5303
Abstract: We consider the multiarmed bandit problem (the problem of Markov bandits) with switching penalties and no discounting in case when state spaces of all bandits are finite. An optimal strategy should have the largest average reward per unit time on an infinite time horizon. For this problem it is shown that an optimal strategy can be specified by a Gittins index under the natural assumption that the switching penalties are nonnegative.
Keywords: multicomponent systems, Gittins index, simple family of alternative Markov bandit processes, multiarmed bandit problem, Markov decision process, controlled Markov processes, long run average return, no discounting, switching penalties, optimal strategy.
Funding agency Grant number
Russian Science Foundation 17-11-01173
This work was supported by the Russian Science Foundation (grant 17-11-01173).
Received: 26.03.2019
Accepted: 20.06.2019
English version:
Theory of Probability and its Applications, 2019, Volume 64, Issue 3, Pages 355–364
DOI: https://doi.org/10.1137/S0040585X97T989544
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: M. P. Savelov, “Gittins index for simple family of Markov bandit processes with switching cost and no discounting”, Teor. Veroyatnost. i Primenen., 64:3 (2019), 442–455; Theory Probab. Appl., 64:3 (2019), 355–364
Citation in format AMSBIB
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    • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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