Actively Learning Gaussian Process Dynamics

22 Nov 2019Mona Buisson-FenetFriedrich SolowjowSebastian Trimpe

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... (read more)

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