Closed-loop Rescheduling using Deep Reinforcement Learning

dc.creatorPalombarini, Jorge A.
dc.creatorMartínez, Ernesto C.
dc.date2019-09
dc.date2019
dc.date2020-02-20T17:09:39Z
dc.date.accessioned2023-07-14T18:31:00Z
dc.date.available2023-07-14T18:31:00Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/89513
dc.identifierissn:2618-3277
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7431714
dc.descriptionIn this work, a novel approach for generating rescheduling knowledge which can be used in real-time for handling unforeseen events without extra deliberation is presented. For generating such control knowledge, the rescheduling task is modelled and solved as a closed-loop control problem by resorting to the integration of a schedule state simulator with a rescheduling agent that can learn successful schedule repairing policies directly from a variety of simulated transitions between schedule states, using as input readily available schedule color-rich Gantt chart images, and negligible prior knowledge. The generated knowledge is stored in a deep Q-network, which can be used as a computational tool in a closed-loop rescheduling control way that select repair actions to make progress towards a goal schedule state, without requiring to compute the rescheduling problem solution every time a disruptive event occurs and safely generalize control knowledge to unseen schedule states.
dc.descriptionSociedad Argentina de Informática e Investigación Operativa
dc.formatapplication/pdf
dc.format86
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-sa/3.0/
dc.rightsCreative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
dc.subjectCiencias Informáticas
dc.subjectControl knowledge
dc.subjectSchedule state simulator
dc.subjectComputational tool
dc.titleClosed-loop Rescheduling using Deep Reinforcement Learning
dc.typeObjeto de conferencia
dc.typeResumen


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