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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】: 國家自然科學基金項目(No.61703369);; 浙江省重點研發計劃項目(No.2017C03039);; 溫州市重大科技專項項目(No.ZS2017007)資助~~
【CateGory Index】: O232
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