ガウス過程ダイナミクスの能動学習
Actively Learning Gaussian Process Dynamics
ガウス過程回帰の情報理論的特性を活かし、系のダイナミクス制約を守りつつ不確実性の高い領域を選んで効率的に力学系を学習する能動学習手法を提案し、数値ベンチマークで検証した。
著者: Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
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 finally verified in an extensive numerical benchmark.
