Markov Chain Monte Carlo for Koopman-based Optimal Control: Technical Report
Optimization and Control
2024-07-10 v2
Abstract
We propose a Markov Chain Monte Carlo (MCMC) algorithm based on Gibbs sampling with parallel tempering to solve nonlinear optimal control problems. The algorithm is applicable to nonlinear systems with dynamics that can be approximately represented by a finite dimensional Koopman model, potentially with high dimension. This algorithm exploits linearity of the Koopman representation to achieve significant computational saving for large lifted states. We use a video-game to illustrate the use of the method.
Cite
@article{arxiv.2405.01788,
title = {Markov Chain Monte Carlo for Koopman-based Optimal Control: Technical Report},
author = {João Hespanha and Kerem Camsari},
journal= {arXiv preprint arXiv:2405.01788},
year = {2024}
}