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《Pattern Recognition and Artificial Intelligence》 2019-04
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Data Driven Optimal Stabilization Control and Simulation Based on Reinforcement Learning

LU Chaolun;LI Yongqiang;FENG Yuanjing;College of Information Engineering,Zhejiang University of Technology;  
Q-learning algorithm is used to solve the optimal stabilization control problem while only the data, rather than the model of the plant, is available. Due to the continuity of state space and control space, Q-learning can only be implemented in an approximate manner. Therefore, the proposed approximate Q-learning algorithm can obtain only one suboptimal controller. Although the obtained controller is suboptimal, the simulation shows that the closed-loop domain of attraction of the proposed algorithm is broader and the cost function is also smaller than the linear quadratic regulator and deep deterministic policy gradient method for the strongly nonlinear plant.
【Fund】: 国家自然科学基金项目(No.61703369);; 浙江省重点研发计划项目(No.2017C03039);; 温州市重大科技专项项目(No.ZS2017007)资助~~
【CateGory Index】: O232
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