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Document type:
Konferenzbeitrag 
Contribution type:
Textbeitrag / Aufsatz 
Author(s):
A. Capone; G.Noske; J. Umlauft; T. Beckers; A. Lederer; S. Hirche 
Title:
Localized active learning of Gaussian process state space models 
Abstract:
While most dynamic system exploration techniques aim to achieve a globally accurate model, this is generally unsuited for systems with unbounded state spaces. Furthermore, many applications do not require a globally accurate model, e.g., local stabilization tasks. In this paper, we propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. Our approach aims to maximize the mutual information...    »
 
Keywords:
data_driven_control 
Editor:
Proceedings of Machine Learning Research 
Book / Congress title:
Learning for Dynamics & Control 
Year:
2020 
Year / month:
2020-06 
Month:
Jun 
Reviewed:
ja