Gaussian Process Planning with Lipschitz Continuous Reward Functions

Gaussian Process Planning with Lipschitz Continuous Reward Functions

This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desi...

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Bibliografiska uppgifter
Huvudupphovsmän: Ling, Chun Kai, Low, Kian Hsiang, Jaillet, Patrick
Övriga upphovsmän: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Materialtyp: Artikel
Språk:en_US
Publicerad: Association for Computing Machinery 2017
Länkar:http://hdl.handle.net/1721.1/112929
https://orcid.org/0000-0002-8585-6566

Internet

http://hdl.handle.net/1721.1/112929
https://orcid.org/0000-0002-8585-6566

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