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• W3036320002 abstract "While the identification of nonlinear dynamical systems is a fundamental building block of model-based reinforcement learning and feedback control, its sample complexity is only understood for systems that either have discrete states and actions or for systems that can be identified from data generated by i.i.d. random inputs. Nonetheless, many interesting dynamical systems have continuous states and actions and can only be identified through a judicious choice of inputs. Motivated by practical settings, we study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. To estimate such systems in finite time identification methods must explore all directions in feature space. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression." @default.
• W3036320002 created "2020-06-25" @default.
• W3036320002 creator A5012870568 @default.
• W3036320002 creator A5049812527 @default.
• W3036320002 creator A5062059992 @default.
• W3036320002 date "2020-06-18" @default.
• W3036320002 modified "2023-09-27" @default.
• W3036320002 title "Active Learning for Nonlinear System Identification with Guarantees." @default.
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• W3036320002 hasPublicationYear "2020" @default.
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