Abstract
This paper considers collaborative system identification when agents have incomplete local measurements. It combines Koopman lifting, deep neural networks, and consensus coordination so agents exchange lifted-state information and reconstruct global nonlinear dynamics. Experiments on Lunar Lander show performance close to centralized deep Koopman learning with full-state access.
Demo
Demo coming soon.
Citation
Hao, Wenjian, Lili Wang, Ayush Rai, and Shaoshuai Mou. 2024. "Distributed Koopman Learning with Incomplete Measurements." arXiv preprint arXiv:2409.11586.
@techreport{WHao2024distribu,
author = {W Hao, L Wang, A Rai, S Mou},
year = {2024},
title = {Distributed Koopman Learning with Incomplete Measurements},
number = {arXiv:2409.11586},
url = {https://arxiv.org/pdf/2409.11586}
}