Data-Driven Control of Nonlinear Systems: Learning Koopman Operators for Policy Gradient

Abstract

Data-driven control of nonlinear dynamical systems is a largely open problem. In this paper, building upon the theory of Koopman operators and exploiting ideas from policy gradient methods in reinforcement learning, a novel approach for data-driven optimal control of unknown nonlinear dynamical systems is introduced.