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Combinatorial pure exploration with continuous and separable reward functions and its applications
KTH.
2018 (English)In: IJCAI'18 Proceedings of the 27th International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2018, p. 2291-2297Conference paper, Published paper (Refereed)
Abstract [en]

We study the Combinatorial Pure Exploration problem with Continuous and Separable reward functions (CPE-CS) in the stochastic multi-armed bandit setting. In a CPE-CS instance, we are given several stochastic arms with unknown distributions, as well as a collection of possible decisions. Each decision has a reward according to the distributions of arms. The goal is to identify the decision with the maximum reward, using as few arm samples as possible. The problem generalizes the combinatorial pure exploration problem with linear rewards, which has attracted significant attention in recent years. In this paper, we propose an adaptive learning algorithm for the CPE-CS problem, and analyze its sample complexity. In particular, we introduce a new hardness measure called the consistent optimality hardness, and give both the upper and lower bounds of sample complexity. Moreover, we give examples to demonstrate that our solution has the capacity to deal with non-linear reward functions.

Place, publisher, year, edition, pages
International Joint Conferences on Artificial Intelligence , 2018. p. 2291-2297
Series
IJCAI International Joint Conference on Artificial Intelligence, ISSN 1045-0823
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-246546Scopus ID: 2-s2.0-85055687549ISBN: 9780999241127 (print)OAI: oai:DiVA.org:kth-246546DiVA, id: diva2:1297516
Conference
27th International Joint Conference on Artificial Intelligence, IJCAI 2018, 13 July 2018 through 19 July 2018
Note

QC 20190320

Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2019-03-20Bibliographically approved

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Ok, Jungseul

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  • apa
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  • ieee
  • modern-language-association-8th-edition
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More styles
Language
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  • nn-NB
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  • Other locale
More languages
Output format
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