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Recently, reinforcement learning and evolution strategy have become major tools in the field of machine learning, and have shown excellent performance in various engineering problems. In particular, the Natural Actor-Critic (NAC) approach and the Natural Evolution Strategies (NES) have led to considerable interests in the area of natural-gradient-based machine learning methods with many successful applications. In this paper, we apply the NAC and the NES to pathtracking control problems for autonomous vehicles. Simulation results show that these methods can yield better performance compared to the conventional PID controllers.

The moving behavior of particle with voltage biasing is studied by analyzing the displacement current generated in electrodes and the drift current by moving particles in cell gap. These currents are ascertained by optical reflectivity on the panel. We obtained the saturated current after a peak in threshold voltage which is coincide with reflectivity of 80%. These saturated optical reflectivity and its drift current offer optimum q/m of particles and driving voltage and can be analytically studied on grey scale methods. Especially regional analysis is useful to aging and driving voltage and the understanding of operating mechanism of charged particle type display.