Information Theoretic Regret Bounds for Online Nonlinear Control

arXiv2006.12466MaRDI QIDQ6343475

Author name not available (Why is that?)

Publication date: 22 June 2020

Abstract: This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Reproducing Kernel Hilbert Space. This framework yields a general setting that permits discrete and continuous control inputs as well as non-smooth, non-differentiable dynamics. Our main result, the Lower Confidence-based Continuous Control ($L{C}^3$) algorithm, enjoys a near-optimal $O(sqrtT)$ regret bound against the optimal controller in episodic settings, where $T$ is the number of episodes. The bound has no explicit dependence on dimension of the system dynamics, which could be infinite, but instead only depends on information theoretic quantities. We empirically show its application to a number of nonlinear control tasks and demonstrate the benefit of exploration for learning model dynamics.

Has companion code repository: https://github.com/Mohnishi/LC3

This page was built for publication: Information Theoretic Regret Bounds for Online Nonlinear Control

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6343475)