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Método de error de Bellman con ponderación de volumen para mallado adaptativo en programación dinámica aproximada

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Método de error de Bellman con ponderación de volumen para mallado adaptativo en programación dinámica aproximada

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Armesto, L.; Sala, A. (2021). Método de error de Bellman con ponderación de volumen para mallado adaptativo en programación dinámica aproximada. Revista Iberoamericana de Automática e Informática industrial. 19(1):37-47. https://doi.org/10.4995/riai.2021.15698

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/178687

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Title: Método de error de Bellman con ponderación de volumen para mallado adaptativo en programación dinámica aproximada
Secondary Title: Volume-weighted Bellman error method for adaptive meshing in approximate dynamic programming
Author: Armesto, Leopoldo Sala, Antonio
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica
Issued date:
Abstract:
[EN] Optimal control and reinforcement learning have an associate “value function” which must be suitably approximated. Value function approximation problems usually have different precision requirements in different regions ...[+]


[ES] El control óptimo y aprendizaje por refuerzo lleva asociada una "función de valor'' que debe ser adecuadamente aproximada. Estos problemas de aproximar funciones de valor tienen, usualmente, diferentes requerimientos ...[+]
Subjects: Control inteligente , Programación Dinámica Aproximada , Control Óptimo , Aprendizaje , Intelligent control , Approximate dynamic programming , Optimal control , Neural learning
Copyrigths: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Source:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2021.15698
Publisher:
Universitat Politècnica de València
Publisher version: https://doi.org/10.4995/riai.2021.15698
Project ID:
info:eu-repo/grantAgreement/AEI//PID2020-116585GB-I00/
Thanks:
Este artículo ha sido financiado por la Agencia Española de Investigación mediante el proyecto del Plan Nacional PID2020-116585GB-I00.
Type: Artículo

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