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- Q2882343 subject Q7015116.
- Q2882343 subject Q8730192.
- Q2882343 subject Q8730194.
- Q2882343 subject Q8818222.
- Q2882343 abstract "In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a gambler at a row of slot machines (sometimes known as "one-armed bandits") has to decide which machines to play, how many times to play each machine and in which order to play them. When played, each machine provides a random reward from a probability distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in "some aspects of the sequential design of experiments".A theorem, the Gittins index published first by John C. Gittins gives an optimal policy in the Markov setting for maximizing the expected discounted reward.In practice, multi-armed bandits have been used to model the problem of managing research projects in a large organization, like a science foundation or a pharmaceutical company. Given a fixed budget, the problem is to allocate resources among the competing projects, whose properties are only partially known at the time of allocation, but which may become better understood as time passes.In early versions of the multi-armed bandit problem, the gambler has no initial knowledge about the machines. The crucial tradeoff the gambler faces at each trial is between "exploitation" of the machine that has the highest expected payoff and "exploration" to get more information about the expected payoffs of the other machines. The trade-off between exploration and exploitation is also faced in reinforcement learning.".
- Q2882343 thumbnail Las_Vegas_slot_machines.jpg?width=300.
- Q2882343 wikiPageExternalLink bandit.sourceforge.net.
- Q2882343 wikiPageExternalLink banditSurvey.pdf.
- Q2882343 wikiPageExternalLink 10.1007%2F978-3-642-34487-9_40.
- Q2882343 wikiPageExternalLink 415.
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- Q2882343 wikiPageExternalLink bandit_algorithms_vs_ab.html.
- Q2882343 wikiPageExternalLink contextual_bandit_survey.pdf.
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- Q2882343 wikiPageExternalLink Feynmans_restaurant_problem.html.
- Q2882343 wikiPageExternalLink banditlib.
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- Q2882343 comment "In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a gambler at a row of slot machines (sometimes known as "one-armed bandits") has to decide which machines to play, how many times to play each machine and in which order to play them. When played, each machine provides a random reward from a probability distribution specific to that machine.".
- Q2882343 label "Multi-armed bandit".
- Q2882343 depiction Las_Vegas_slot_machines.jpg.