Autonomous Vehicle Path Tracking Based on Natural Gradient Methods
Ki-Young Kwon*, Keun-Woo Jung**, Dong-Su Yang*,
and Jooyoung Park*
*Department of Control and Instrumentation Engineering, Korea University, Sejong-ro 2511, Sejong 339-700, Korea
**LG Electronics, Gasan-dong, Geumcheon-gu, Seoul 153-802, Korea
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.
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