Relevant bibliographies by topics / Multiarmed Bandits

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  1. Journal articles

Academic literature on the topic 'Multiarmed Bandits'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Multiarmed Bandits"

1

Righter, Rhonda, and J. George Shanthikumar. "Independently Expiring Multiarmed Bandits." Probability in the Engineering and Informational Sciences 12, no. 4 (1998): 453–68. http://dx.doi.org/10.1017/s0269964800005325.

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We give conditions on the optimality of an index policy for multiarmed bandits when arms expire independently. We also give a new simple proof of the optimality of the Gittins index policy for the classic multiarmed bandit problem.
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2

Gao, Xiujuan, Hao Liang, and Tong Wang. "A Common Value Experimentation with Multiarmed Bandits." Mathematical Problems in Engineering 2018 (July 30, 2018): 1–8. http://dx.doi.org/10.1155/2018/4791590.

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We study a value common experimentation with multiarmed bandits and give an application about the experimentation. The second derivative of value functions at cutoffs is investigated when an agent switches action with multiarmed bandits. If consumers have identical preference which is unknown and purchase products from only two sellers among multiple sellers, we obtain the necessary and sufficient conditions about the common experimentation. The Markov perfect equilibrium and the socially effective allocation in K-armed markets are discussed.
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3

Kalathil, Dileep, Naumaan Nayyar, and Rahul Jain. "Decentralized Learning for Multiplayer Multiarmed Bandits." IEEE Transactions on Information Theory 60, no. 4 (2014): 2331–45. http://dx.doi.org/10.1109/tit.2014.2302471.

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4

Cesa-Bianchi, Nicolò. "MULTIARMED BANDITS IN THE WORST CASE." IFAC Proceedings Volumes 35, no. 1 (2002): 91–96. http://dx.doi.org/10.3182/20020721-6-es-1901.01001.

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5

Bray, Robert L., Decio Coviello, Andrea Ichino, and Nicola Persico. "Multitasking, Multiarmed Bandits, and the Italian Judiciary." Manufacturing & Service Operations Management 18, no. 4 (2016): 545–58. http://dx.doi.org/10.1287/msom.2016.0586.

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6

Denardo, Eric V., Haechurl Park, and Uriel G. Rothblum. "Risk-Sensitive and Risk-Neutral Multiarmed Bandits." Mathematics of Operations Research 32, no. 2 (2007): 374–94. http://dx.doi.org/10.1287/moor.1060.0240.

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7

Weber, Richard. "On the Gittins Index for Multiarmed Bandits." Annals of Applied Probability 2, no. 4 (1992): 1024–33. http://dx.doi.org/10.1214/aoap/1177005588.

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8

Drugan, Madalina M. "Covariance Matrix Adaptation for Multiobjective Multiarmed Bandits." IEEE Transactions on Neural Networks and Learning Systems 30, no. 8 (2019): 2493–502. http://dx.doi.org/10.1109/tnnls.2018.2885123.

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9

Burnetas, Apostolos N., and Michael N. Katehakis. "ASYMPTOTIC BAYES ANALYSIS FOR THE FINITE-HORIZON ONE-ARMED-BANDIT PROBLEM." Probability in the Engineering and Informational Sciences 17, no. 1 (2003): 53–82. http://dx.doi.org/10.1017/s0269964803171045.

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The multiarmed-bandit problem is often taken as a basic model for the trade-off between the exploration and utilization required for efficient optimization under uncertainty. In this article, we study the situation in which the unknown performance of a new bandit is to be evaluated and compared with that of a known one over a finite horizon. We assume that the bandits represent random variables with distributions from the one-parameter exponential family. When the objective is to maximize the Bayes expected sum of outcomes over a finite horizon, it is shown that optimal policies tend to simple limits when the length of the horizon is large.
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10

Nayyar, Naumaan, Dileep Kalathil, and Rahul Jain. "On Regret-Optimal Learning in Decentralized Multiplayer Multiarmed Bandits." IEEE Transactions on Control of Network Systems 5, no. 1 (2018): 597–606. http://dx.doi.org/10.1109/tcns.2016.2635380.

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