Active Learning in Gaussian Process State Space Model

We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we...

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Bibliographic Details
Published inarXiv.org
Main Authors Hon Sum Alec Yu, Yao, Dingling, Zimmer, Christoph, Toussaint, Marc, Nguyen-Tuong, Duy
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.07.2021
Subjects
Active learning
Gaussian process
Learning
Nonlinear dynamics
State space models
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Summary:We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual information as our active learning criterion. In particular, we present two approaches for the approximation of mutual information for the GPSSM given latent states. The proposed approaches are evaluated in several physical systems where we actively learn the underlying non-linear dynamics represented by the state-space model.
ISSN:2331-8422