On the Design of LQR Kernels for Efficient Controller Learning
2017
Conference Paper
am
ics
pn
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.
| Author(s): | Alonso Marco and Philipp Hennig and Stefan Schaal and Sebastian Trimpe |
| Book Title: | Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC) |
| Pages: | 5193--5200 |
| Year: | 2017 |
| Month: | December |
| Day: | 12-15 |
| Publisher: | IEEE |
| Department(s): | Autonomous Motion, Intelligent Control Systems, Probabilistic Numerics |
| Research Project(s): |
Controller Learning using Bayesian Optimization
Bayesian Optimization |
| Bibtex Type: | Conference Paper (conference) |
| Paper Type: | Conference |
| DOI: | 10.1109/CDC.2017.8264429 |
| Event Name: | IEEE Conference on Decision and Control |
| Event Place: | Melbourne, VIC, Australia |
| State: | Published |
| Links: |
arXiv
On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation |
| Video: | |
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BibTex @conference{MaHeScTr17,
title = {On the Design of {LQR} Kernels for Efficient Controller Learning},
author = {Marco, Alonso and Hennig, Philipp and Schaal, Stefan and Trimpe, Sebastian},
booktitle = {Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC)},
pages = {5193--5200},
publisher = {IEEE},
month = dec,
year = {2017},
doi = {10.1109/CDC.2017.8264429},
month_numeric = {12}
}
|
|
