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Reinforcement Learning in Queues

Abstract : Introduction: Control and optimization in queues have been an active area of research for decades, see for instance [10,11]. Most of the literature up to the present has focused on the model-based setting, a term used to describe the situation in which a model is known. In the coming years, we will witness a huge interest from the community in the model-free approach, a setting that does not assume knowledge of an exact underlying mathematical model. In this short note I provide a personal view of some of the challenges that lie ahead in the transition from model-based to model-free solutions in a queueing context.
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Contributor : Urtzi Ayesta Connect in order to contact the contributor
Submitted on : Thursday, September 1, 2022 - 2:08:48 PM
Last modification on : Friday, September 2, 2022 - 3:51:44 AM


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Urtzi Ayesta. Reinforcement Learning in Queues. Queueing Systems, 2022, Special Issue, 100, pp.497-499. ⟨10.1007/s11134-022-09844-w⟩. ⟨hal-03766768⟩



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