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首页> 外文期刊>International Journal of Adaptive Control and Signal Processing >Adaptive critic design with graph Laplacian for online learning control of nonlinear systems
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Adaptive critic design with graph Laplacian for online learning control of nonlinear systems

机译：图Laplacian的自适应评论家设计用于非线性系统的在线学习控制。

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摘要

In recent years, reinforcement learning (RL) and approximate dynamic programming (ADP) have been widely studied in the community of artificial intelligence and machine learning. As an important class of RL and ADP methods, adaptive critic designs (ACDs) with function approximation have been studied to realize online learning control of nonlinear dynamical systems. However, how to construct efficient feature representations for approximating value functions or policies is still a difficult problem. In this paper, ACDs with graph Laplacian (GL) are proposed by integrating manifold learning methods into feature representations of ACDs. An online learning control algorithm called graph Laplacian dual heuristic programming (GL-DHP) is presented, and its performance is analyzed both theoretically and empirically. Because of the nonlinear approximation ability of feature representation with GL, the GL-DHP method has much better performance than previous DHP methods with manually designed neural networks. Simulation results on learning control of a ball and plate system, which is a typical nonlinear dynamical system with continuous state and action spaces, demonstrate the effectiveness of the GL-DHP method.
机译：近年来，强化学习（RL）和近似动态编程（ADP）在人工智能和机器学习社区中得到了广泛的研究。作为RL和​​ADP方法的重要一类，已经研究了具有函数逼近的自适应批评家设计（ACD），以实现非线性动力学系统的在线学习控制。然而，如何构造有效的特征表示以近似值函数或策略仍然是一个难题。在本文中，通过将流形学习方法集成到ACD的特征表示中，提出了具有图拉普拉斯（GL）图的ACD。提出了一种在线学习控制算法，称为图拉普拉斯对偶启发式编程（GL-DHP），并从理论和经验上对其性能进行了分析。由于使用GL进行特征表示的非线性逼近能力，GL-DHP方法的性能比以前使用人工设计的神经网络的DHP方法要好得多。球和板系统的学习控制的仿真结果证明了GL-DHP方法的有效性，该系统是具有连续状态和动作空间的典型非线性动力学系统。

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