Timezone: »

Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade · Akshay Krishnamurthy · Kendall Lowrey · Motoya Ohnishi · Wen Sun

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #570
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 (LC3) algorithm, enjoys a near-optimal $O(\sqrt{T})$ 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.

Author Information

Sham Kakade (University of Washington & Microsoft Research)
Akshay Krishnamurthy (Microsoft)
Kendall Lowrey (University of Washington)
Motoya Ohnishi (Paul G. Allen School of Computer Science & Engineering)
Wen Sun (Cornell University)

More from the Same Authors