Title data
Sperl, Mario ; Saluzzi, Luca ; Grüne, Lars ; Kalise, Dante:
Separable approximations of optimal value functions under a decaying sensitivity assumption.
In:
62nd IEEE Conference on Decision and Control (CDC) 2023. 
Singapore, Singapore
,
2023
.  pp. 259264
DOI: https://doi.org/10.1109/CDC49753.2023.10383497
This is the latest version of this item.
Project information
Project title: 
Project's official title Project's id Curseofdimensionalityfree nonlinear optimal feedback control with deep neural networks. A compositionalitybased approach via HamiltonJacobiBellman PDEs 463912816 

Project financing: 
Deutsche Forschungsgemeinschaft 
Abstract in another language
An efficient 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 curseofdimensionality 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)
 Separable approximations of optimal value functions under a decaying sensitivity assumption. (deposited 02 Feb 2024 11:10) [Currently Displayed]