Information Theoretic Regret Bounds for Online Nonlinear Control

06/22/2020 ∙ by Sham Kakade, et al. ∙ 14

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 (LC^3) algorithm, enjoys a near-optimal O(√(T)) 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 13

page 36

Code Repositories

LC3

Information Theoretic Regret Bounds for Online Nonlinear Control


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.