Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico

El 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...

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Fecha de publicación:
2021
Institución:
Universidad del Rosario
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Repositorio EdocUR - U. Rosario
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spa
OAI Identifier:
oai:repository.urosario.edu.co:10336/31624
Acceso en línea:
https://doi.org/10.48713/10336_31624
https://repository.urosario.edu.co/handle/10336/31624
Palabra clave:
Aprendizaje por refuerzo cuántico
Toma de decisiones
Aprendizaje por refuerzo
Ingeniería & operaciones afines
Quantum reinforcement learning
Value-based decision-making
Iowa Gambling
Task
Reinforcement learning
Diseño en ingeniería
Rights
License
Abierto (Texto Completo)
id EDOCUR2_dc176941418dcda509195b13d6b1b57e
oai_identifier_str oai:repository.urosario.edu.co:10336/31624
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.spa.fl_str_mv Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
dc.title.TranslatedTitle.spa.fl_str_mv Decision-making model using quantum reinforcement learning
title Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
spellingShingle Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
Aprendizaje por refuerzo cuántico
Toma de decisiones
Aprendizaje por refuerzo
Ingeniería & operaciones afines
Quantum reinforcement learning
Value-based decision-making
Iowa Gambling
Task
Reinforcement learning
Diseño en ingeniería
title_short Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
title_full Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
title_fullStr Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
title_full_unstemmed Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
title_sort Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico
dc.contributor.advisor.none.fl_str_mv López López, Juan Manuel
León Anhuaman, Laura Andrea
dc.contributor.gruplac.spa.fl_str_mv GiBiome
dc.subject.spa.fl_str_mv Aprendizaje por refuerzo cuántico
Toma de decisiones
Aprendizaje por refuerzo
topic Aprendizaje por refuerzo cuántico
Toma de decisiones
Aprendizaje por refuerzo
Ingeniería & operaciones afines
Quantum reinforcement learning
Value-based decision-making
Iowa Gambling
Task
Reinforcement learning
Diseño en ingeniería
dc.subject.ddc.spa.fl_str_mv Ingeniería & operaciones afines
dc.subject.keyword.spa.fl_str_mv Quantum reinforcement learning
Value-based decision-making
Iowa Gambling
Task
Reinforcement learning
dc.subject.lemb.spa.fl_str_mv Diseño en ingeniería
description El 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.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-06-16T20:16:55Z
dc.date.available.none.fl_str_mv 2021-06-16T20:16:55Z
dc.date.created.none.fl_str_mv 2021-05-27
dc.type.eng.fl_str_mv bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.document.spa.fl_str_mv Trabajo de grado
dc.type.spa.spa.fl_str_mv Trabajo de grado
dc.identifier.doi.none.fl_str_mv https://doi.org/10.48713/10336_31624
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/31624
url https://doi.org/10.48713/10336_31624
https://repository.urosario.edu.co/handle/10336/31624
dc.language.iso.spa.fl_str_mv spa
language spa
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dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
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dc.format.extent.spa.fl_str_mv 50 pp.
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad del Rosario
dc.publisher.department.spa.fl_str_mv Escuela de Medicina y Ciencias de la Salud
dc.publisher.program.spa.fl_str_mv Ingeniería Biomédica
institution Universidad del Rosario
dc.source.bibliographicCitation.none.fl_str_mv Sutton, Richard S; Barto, Andrew G (2018) Reinforcement learning: An introduction. : MIT press; 0262352702;
Sutton, Richard S (1988) Learning to predict by the methods of temporal differences. En:Machine learning; Vol. 3; No. 1; pp. 9 - 44; Springer;
Grover, Lov K (1997) Quantum mechanics helps in searching for a needle in a haystack. En:Physical review letters; Vol. 79; No. 2; pp. 325 - 325; APS;
Chen, Chun-Lin; Dong, Dao-Yi (2008) Superposition-inspired reinforcement learning and quantum reinforcement learning. En:Reinforcement Learning; IntechOpen;
Shankar, Ramamurti (2012) Principles of quantum mechanics. : Springer Science & Business Media; 147570576X;
Kaelbling, Leslie Pack; Littman, Michael L; Moore, Andrew W (1996) Reinforcement learning: A survey. En:Journal of artificial intelligence research; Vol. 4; pp. 237 - 285;
Erev, Ido; Barron, Greg (2005) On adaptation, maximization, and reinforcement learning among cognitive strategies. En:Psychological review; Vol. 112; No. 4; pp. 912 - 912; American Psychological Association;
Ekhtiari, Hamed; Paulus, Martin (2016) Neuroscience for Addiction Medicine: From Prevention to Rehabilitation-Methods and Interventions. : Elsevier; 0444637400;
Redgrave, Peter; Prescott, Tony J; Gurney, Kevin (1999) The basal ganglia: a vertebrate solution to the selection problem?. En:Neuroscience; Vol. 89; No. 4; pp. 1009 - 1023; Elsevier;
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Haines, Nathaniel; Vassileva, Jasmin; Ahn, Woo-Young (2018) The Outcome-Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task. En:Cognitive Science; Vol. 42; No. 8; pp. 2534 - 2561; John Wiley & Sons, Ltd; Disponible en: https://doi.org/10.1111/cogs.12688. Disponible en: https://doi.org/10.1111/cogs.12688.
Ahn, Woo-Young; Busemeyer, Jerome R; Wagenmakers, Eric-Jan; Stout, Julie C (2008) Comparison of Decision Learning Models Using the Generalization Criterion Method. En:Cognitive Science; Vol. 32; No. 8; pp. 1376 - 1402; John Wiley & Sons, Ltd; Disponible en: https://doi.org/10.1080/03640210802352992. Disponible en: https://doi.org/10.1080/03640210802352992.
Dong, D; Chen, C; Li, H; Tarn, T (2008) Quantum Reinforcement Learning. En:IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics); Vol. 38; No. 5; pp. 1207 - 1220; Disponible en: 10.1109/TSMCB.2008.925743.
Ahn, Woo-Young; Haines, Nathaniel; Zhang, Lei (2017) Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package. En:Computational Psychiatry; Vol. 1; pp. 24 - 57; Disponible en: https://doi.org/10.1162/CPSY_a_00002. Disponible en: 10.1162/CPSY_a_00002.
Doya, Kenji (2000) Reinforcement Learning in Continuous Time and Space. En:Neural Computation; Vol. 12; No. 1; pp. 219 - 245; Disponible en: https://doi.org/10.1162/089976600300015961. Disponible en: 10.1162/089976600300015961.
Rescorla, R; Wagner, Allan (1972) A theory of Pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement. En:Classical Conditioning: Current Research and Theory;
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Busemeyer, Jerome R; Bruza, Peter D (2012) Quantum Models of Cognition and Decision. Cambridge: Cambridge University Press; 9781107011991; Disponible en: https://www.cambridge.org/core/books/quantum-models-of-cognition-and-decision/75909428F710F7C6AF7D580CB83443AC. Disponible en: DOI: 10.1017/CBO9780511997716.
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Ahn, Woo-Young; Vasilev, Georgi; Lee, Sung-Ha; Busemeyer, Jerome R; Kruschke, John K; Bechara, Antoine; Vassileva, Jasmin (2014) Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. En:Frontiers in Psychology; Vol. 5; pp. 849 - 849; 1664-1078; Disponible en: https://www.frontiersin.org/article/10.3389/fpsyg.2014.00849.
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Erev, Ido; Roth, Alvin E (1998) Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria. En:The American Economic Review; Vol. 88; No. 4; pp. 848 - 881; American Economic Association; Disponible en: http://www.jstor.org/stable/117009.
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Rangel, Antonio; Camerer, Colin; Montague, P Read (2008) A framework for studying the neurobiology of value-based decision making. En:Nature Reviews Neuroscience; Vol. 9; No. 7; pp. 545 - 556; Disponible en: https://doi.org/10.1038/nrn2357. Disponible en: 10.1038/nrn2357.
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Silver, David; Huang, Aja; Maddison, Chris J; Guez, Arthur; Sifre, Laurent; van den Driessche, George; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis (2016) Mastering the game of Go with deep neural networks and tree search. En:Nature; Vol. 529; No. 7587; pp. 484 - 489; Disponible en: https://doi.org/10.1038/nature16961. Disponible en: 10.1038/nature16961.
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spelling López López, Juan Manuela34ddbcd-d51d-4e40-a61e-3baa9d32826d600León Anhuaman, Laura Andrea45fef0a4-6c2d-43f6-8ca6-925469122e7c600GiBiomeSastoque Granados, SantiagoIngeniero BiomédicoFull time4f37677a-e9c0-40d4-9f36-3f28d10c732c6002021-06-16T20:16:55Z2021-06-16T20:16:55Z2021-05-27El 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.Classical 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.50 pp.application/pdfhttps://doi.org/10.48713/10336_31624 https://repository.urosario.edu.co/handle/10336/31624spaUniversidad del RosarioEscuela de Medicina y Ciencias de la SaludIngeniería BiomédicaAbierto (Texto Completo)EL 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.http://purl.org/coar/access_right/c_abf2 Sutton, Richard S; Barto, Andrew G (2018) Reinforcement learning: An introduction. : MIT press; 0262352702; Sutton, Richard S (1988) Learning to predict by the methods of temporal differences. En:Machine learning; Vol. 3; No. 1; pp. 9 - 44; Springer; Grover, Lov K (1997) Quantum mechanics helps in searching for a needle in a haystack. En:Physical review letters; Vol. 79; No. 2; pp. 325 - 325; APS; Chen, Chun-Lin; Dong, Dao-Yi (2008) Superposition-inspired reinforcement learning and quantum reinforcement learning. En:Reinforcement Learning; IntechOpen; Shankar, Ramamurti (2012) Principles of quantum mechanics. : Springer Science & Business Media; 147570576X; Kaelbling, Leslie Pack; Littman, Michael L; Moore, Andrew W (1996) Reinforcement learning: A survey. En:Journal of artificial intelligence research; Vol. 4; pp. 237 - 285; Erev, Ido; Barron, Greg (2005) On adaptation, maximization, and reinforcement learning among cognitive strategies. En:Psychological review; Vol. 112; No. 4; pp. 912 - 912; American Psychological Association; Ekhtiari, Hamed; Paulus, Martin (2016) Neuroscience for Addiction Medicine: From Prevention to Rehabilitation-Methods and Interventions. : Elsevier; 0444637400; Redgrave, Peter; Prescott, Tony J; Gurney, Kevin (1999) The basal ganglia: a vertebrate solution to the selection problem?. En:Neuroscience; Vol. 89; No. 4; pp. 1009 - 1023; Elsevier; Stoet, Gijsbert (2016) PsyToolkit: A Novel Web-Based Method for Running Online Questionnaires and Reaction-Time Experiments. En:Teaching of Psychology; Vol. 44; No. 1; pp. 24 - 31; SAGE Publications Inc; Disponible en: https://doi.org/10.1177/0098628316677643. Disponible en: 10.1177/0098628316677643. Kondo, Toshiyuki; Ito, Koji (2004) A reinforcement learning with evolutionary state recruitment strategy for autonomous mobile robots control. 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