Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation

We develop data-driven reinforcement learning (RL) control designs for input-affine nonlinear systems. We use Carleman linearization to express the state-space representation of the nonlinear dynamical model in the Carleman space, and develop a real-time algorithm that can learn nonlinear state-feedback controllers using state and input measurements in the infinite-dimensional Carleman space.