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Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets

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Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets

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dc.contributor.author Calabuig, J. M. es_ES
dc.contributor.author Falciani, H. es_ES
dc.contributor.author Sánchez Pérez, Enrique Alfonso es_ES
dc.date.accessioned 2021-09-16T03:31:51Z
dc.date.available 2021-09-16T03:31:51Z
dc.date.issued 2020-07-20 es_ES
dc.identifier.issn 0925-2312 es_ES
dc.identifier.uri http://hdl.handle.net/10251/172597
dc.description.abstract [EN] We consider a quasi-metric topological structure for the construction of a new reinforcement learning model in the framework of financial markets. It is based on a Lipschitz type extension of reward functions defined in metric spaces. Specifically, the McShane and Whitney extensions are considered for a reward function which is defined by the total evaluation of the benefits produced by the investment decision at a given time. We define the metric as a linear combination of a Euclidean distance and an angular metric component. All information about the evolution of the system from the beginning of the time interval is used to support the extension of the reward function, but in addition this data set is enriched by adding some artificially produced states. Thus, the main novelty of our method is the way we produce more states-which we call "dreams"-to enrich learning. Using some known states of the dynamical system that represents the evolution of the financial market, we use our technique to simulate new states by interpolating real states and introducing some random variables. These new states are used to feed a learning algorithm designed to improve the investment strategy by following a typical reinforcement learning scheme. (C) 2020 Elsevier B.V. All rights reserved. es_ES
dc.description.sponsorship This work was supported by the Ministerio de Ciencia, Innovacion y Universidades, Agencial Estatal de Investigaciones and FEDER (Spain) (grant number MTM2016-77054-C2-1-P.) es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Neurocomputing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Pseudo-metric es_ES
dc.subject Reinforcement learning es_ES
dc.subject Lipschitz extension es_ES
dc.subject Mathematical economics es_ES
dc.subject Financial market es_ES
dc.subject Model es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.neucom.2020.02.052 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//MTM2016-77054-C2-1-P/ES/ANALISIS NO LINEAL, INTEGRACION VECTORIAL Y APLICACIONES EN CIENCIAS DE LA INFORMACION/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Calabuig, JM.; Falciani, H.; Sánchez Pérez, EA. (2020). Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets. Neurocomputing. 398:172-184. https://doi.org/10.1016/j.neucom.2020.02.052 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.neucom.2020.02.052 es_ES
dc.description.upvformatpinicio 172 es_ES
dc.description.upvformatpfin 184 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 398 es_ES
dc.relation.pasarela S\424071 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD es_ES
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