Titelangaben
Sperl, Mario ; Saluzzi, Luca ; Grüne, Lars ; Kalise, Dante:
Separable approximations of optimal value functions under a decaying sensitivity assumption.
Bayreuth
,
2023
DOI: https://doi.org/10.48550/arXiv.2304.06379
Eine aktuellere Version dieses Eintrags ist verfügbar.
Angaben zu Projekten
Projekttitel: |
Offizieller Projekttitel Projekt-ID Curse-of-dimensionality-free nonlinear optimal feedback control with deep neural networks. A compositionality-based approach via Hamilton-Jacobi-Bellman PDEs 463912816 |
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Projektfinanzierung: |
Deutsche Forschungsgemeinschaft |
Abstract
A new approach for the construction of separable approximations of optimal value functions from interconnected optimal control problems is presented. The approach is based on assuming decaying sensitivities between subsystems, enabling a curse-of-dimensionality free approximation, for instance by deep neural networks.
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- Separable approximations of optimal value functions under a decaying sensitivity assumption. (deposited 17 Apr 2023 07:43) [Aktuelle Anzeige]