Actively Learning Gaussian Process Dynamics
2020
Conference Paper
ics
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way. We propose active learning strategies that leverage information-theoretical properties arising naturally during Gaussian process regression, while respecting constraints on the sampling process imposed by the system dynamics. Sample points are selected in regions with high uncertainty, leading to exploratory behavior and data-efficient training of the model. All results are verified in an extensive numerical benchmark.
| Author(s): | Mona Buisson-Fenet and Friedrich Solowjow and Sebastian Trimpe |
| Book Title: | Proceedings of the 2nd Conference on Learning for Dynamics and Control |
| Volume: | 120 |
| Pages: | 5--15 |
| Year: | 2020 |
| Month: | June |
| Series: | Proceedings of Machine Learning Research (PMLR) |
| Editors: | Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie |
| Publisher: | PMLR |
| Department(s): | Intelligent Control Systems |
| Bibtex Type: | Conference Paper (conference) |
| Paper Type: | Conference |
| Event Name: | 2nd Annual Conference on Learning for Dynamics and Control (L4DC) |
| Event Place: | The Cloud |
| State: | Published |
| URL: | http://proceedings.mlr.press/v120/buisson-fenet20a.html |
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BibTex @conference{Actively_Learning_Gaussian_Process_Dynamics,
title = {Actively Learning Gaussian Process Dynamics},
author = {Buisson-Fenet, Mona and Solowjow, Friedrich and Trimpe, Sebastian},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
volume = {120},
pages = {5--15},
series = {Proceedings of Machine Learning Research (PMLR)},
editors = {Bayen, Alexandre M. and Jadbabaie, Ali and Pappas, George and Parrilo, Pablo A. and Recht, Benjamin and Tomlin, Claire and Zeilinger, Melanie},
publisher = {PMLR},
month = jun,
year = {2020},
doi = {},
url = {http://proceedings.mlr.press/v120/buisson-fenet20a.html},
month_numeric = {6}
}
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