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dc.contributor.advisorLópez López, Juan Manuel 
dc.contributor.advisorLeón Anhuaman, Laura Andrea 
dc.creatorSastoque Granados, Santiago 
dc.date.accessioned2021-06-16T20:16:55Z
dc.date.available2021-06-16T20:16:55Z
dc.date.created2021-05-27
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/31624
dc.descriptionEl aprendizaje por refuerzo clásico (CRL, por sus siglas en inglés), ha sido utilizado ampliamente en aplicaciones para la psicología y neurociencia. Sin embargo, el aprendizaje por refuerzo cuántico (QRL, por sus siglas en inglés) ha demostrado mejor desempeño en simulaciones por computadora. Para poder analizar la toma de decisiones basada en el valor utilizando estos modelos, se diseñó un protocolo experimental que consiste en dos grupos sanos de diferentes edades realizando la prueba Iowa Gambling Task. Con esta base de datos se comparó el desempeño de cuatro modelos de CRL y uno de QRL, los resultados demostraron que la toma de decisiones basadas en el valor se puede modelar utilizando aprendizaje por refuerzo cuántico y esto sugiere que el enfoque cuántico a la toma de decisiones aporta nuevas perspectivas y herramientas que permiten entender nuevos aspectos del proceso de toma de decisiones humano.
dc.description.abstractClassical reinforcement learning (CRL) has been widely used in psychology and neuroscience applications. However, quantum reinforcement learning (QRL) has shown better performance in computer simulations. In order to analyze value-based decision-making using these models, an experimental protocol was designed, consisting of two healthy groups of different ages performing the Iowa Gambling Task. The results showed that value-based decision making can be modeled using quantum reinforcement learning and this suggests that the quantum approach to decision making provides new perspectives and tools that allow to understand new aspects of the human decision making process.
dc.format.extent50 pp.
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.subjectAprendizaje por refuerzo cuántico
dc.subjectToma de decisiones
dc.subjectAprendizaje por refuerzo
dc.subject.ddcIngeniería & operaciones afines 
dc.subject.lembDiseño en ingeniería
dc.titleModelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
dc.typebachelorThesis
dc.publisherUniversidad del Rosario
dc.creator.degreeIngeniero Biomédico
dc.publisher.programIngeniería Biomédica
dc.publisher.departmentEscuela de Medicina y Ciencias de la Salud
dc.subject.keywordQuantum reinforcement learning
dc.subject.keywordValue-based decision-making
dc.subject.keywordIowa Gambling
dc.subject.keywordTask
dc.subject.keywordReinforcement learning
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.type.spaTrabajo de grado
dc.rights.accesoAbierto (Texto Completo)
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
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dc.rights.licenciaEL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma.
dc.contributor.gruplacGiBiome
dc.type.documentTrabajo de grado
dc.identifier.doihttps://doi.org/10.48713/10336_31624
dc.creator.degreetypeFull time
dc.title.TranslatedTitleDecision-making model using quantum reinforcement learning
dc.source.instnameinstname:Universidad del Rosario
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR


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