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SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Zolman, Nicholas, Lagemann, Christian, Fasel, Urban, Kutz, J. Nathan, Brunton, Steven L.
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Systems and Control Dynamical Systems Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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