Tuning Bandit Algorithms in Stochastic Environments-Reference-Cited by-同舟云学术

Tuning Bandit Algorithms in Stochastic Environments

Published:2007 Issue: Volume: Page:150-165
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Audibert Jean-Yves,Munos Rémi,Szepesvári Csaba

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-75225-7_15

Reference9 articles.

1. Agrawal, R.: Sample mean based index policies with O(logn) regret for the multi-armed bandit problem. Advances in Applied Probability 27, 1054–1078 (1995)

2. Audibert, J.-Y., Munos, R., Szepesvári,Cs.: Variance estimates and exploration function in multi-armed bandit. Research report 07-31, Certis - Ecole des Ponts (2007), http://cermics.enpc.fr/~audibert/RR0731.pdf

3. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite time analysis of the multiarmed bandit problem. Machine Learning 47(2-3), 235–256 (2002)

4. Auer, P., Cesa-Bianchi, N., Shawe-Taylor, J.: Exploration versus exploitation challenge. In: 2nd PASCAL Challenges Workshop. Pascal Network (2006)

5. Gittins, J.C.: Multi-armed Bandit Allocation Indices. In: Wiley-Interscience series in systems and optimization. Wiley, Chichester (1989)

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