Actively Learning Gaussian Process Dynamical Systems Through Global and Local Explorations
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DOI: 10.1109/access.2022.3154095
Abstract: Usually learning dynamical systems by data-driven methods requires large amount of training data, which may be time consuming and expensive. Active learning, which aims at choosing the most informative samples to make learning more efficient… read more here.
Keywords: state; informative trajectory; dynamical systems; state action ... See more keywords