A Data-Driven Approach for Inverse Optimal Control
Abstract
This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its states and inputs. The approach solves the IOC problem in a challenging situation when the system dynamics is unknown. The key idea of the proposed approach comes from the deep Koopman representation of the unknown system, which employs a deep neural network to represent observables for the Koopman operator. By assuming the objective function to be learned is parameterized as a linear combination of features with unknown weights, the proposed approach for IOC is able to achieve a Koopman representation of the unknown dynamics and the unknown weights in objective function together. Simulation is provided to verify the proposed approach.
Keywords
Cite
@article{arxiv.2304.00100,
title = {A Data-Driven Approach for Inverse Optimal Control},
author = {Zihao Liang and Wenjian Hao and Shaoshuai Mou},
journal= {arXiv preprint arXiv:2304.00100},
year = {2023}
}