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UID:393@lincs.fr
DTSTART;TZID=Europe/Paris:20180912T140000
DTEND;TZID=Europe/Paris:20180912T150000
DTSTAMP:20180921T104054Z
URL:https://www.lincs.fr/events/asymptotic-optimal-control-of-markov-modul
 ated-restless-bandits/
SUMMARY:Asymptotic Optimal Control of Markov-Modulated Restless Bandits
DESCRIPTION:In this talk we will discuss optimal control subject to
 changing conditions (a changing environment). This is an area that recently
 received a lot of attention as it arises in numerous situations in
 practice. Some applications being cloud computing systems with fluctuating
 arrival rates\, or the time-varying capacity as encountered in power-aware
 systems or wireless downlink channels. To study this\, we focus on a
 restless bandit model\, which has proved to be a powerful stochastic
 optimization framework to model scheduling of activities. This work is a
 first step to its optimal control when restless bandits are subject to
 changing conditions.\n\nWe consider the restless bandit problem in an
 asymptotic regime\, which is obtained by letting the population of bandits
 grow large\, and letting the environment change relatively fast. We present
 sufficient conditions for a policy to be asymptotically optimal and show
 that a set of priority policies satisfies these. Under an indexability
 assumption\, an averaged version of Whittle's index policy is proved to be
 inside this set of asymptotic optimal policies. The performance of the
 averaged Whittle's index policy is numerically evaluated for a multi-class
 scheduling problem.
CATEGORIES:Seminars,Youtube
LOCATION:LINCS Seminars room\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.828400;2.356897
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS Seminars
 room:geo:48.828400,2.356897
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TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
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DTSTART:20180325T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
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