Dynamical low-rank approximations of solutions to the Hamilton-Jacobi-Bellman equation
Optimization and Control
2021-11-30 v1
Abstract
We present a novel method to approximate optimal feedback laws for nonlinear optimal control based on low-rank tensor train (TT) decompositions. The approach is based on the Dirac-Frenkel variational principle with the modification that the optimisation uses an empirical risk. Compared to current state-of-the-art TT methods, our approach exhibits a greatly reduced computational burden while achieving comparable results. A rigorous description of the numerical scheme and demonstrations of its performance are provided.
Keywords
Cite
@article{arxiv.2111.14540,
title = {Dynamical low-rank approximations of solutions to the Hamilton-Jacobi-Bellman equation},
author = {Martin Eigel and Reinhold Schneider and David Sommer},
journal= {arXiv preprint arXiv:2111.14540},
year = {2021}
}