Adaptive reinforcement learning system for linearization control

A linearization scheme is proposed to demonstrate how a neural network scheme learns to linearize a system without any identification. The process occurs within an evaluator and a controller, which communicate with each other through reinforcement signals. From simulation results, the proposed learning scheme notably surpasses the conventional neural network approaches