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• W3102381603 abstract "In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K finite-state discrete-time Markov chains (arms) with unknown state spaces and statistics. At each time step the player can play M arms. The objective of the user is to decide for each step which M of the K arms to play over a sequence of trials so as to maximize its long term reward. The restless multiarmed bandit is particularly relevant to the application of opportunistic spectrum access (OSA), where a (secondary) user has access to a set of K channels, each of time-varying condition as a result of random fading and/or certain primary users' activities." @default.
• W3102381603 created "2020-11-23" @default.
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• W3102381603 date "2012-08-01" @default.
• W3102381603 modified "2023-10-17" @default.
• W3102381603 title "Online Learning of Rested and Restless Bandits" @default.
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• W3102381603 doi "https://doi.org/10.1109/tit.2012.2198613" @default.
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