Wang Zhanshan, Liu Yingying, Zhang Huaguang
IEEE Trans Cybern. 2024 Sep;54(9):5463-5472. doi: 10.1109/TCYB.2024.3380001. Epub 2024 Aug 26.
In this article, the data-based output consensus of discrete-time multiagent systems under switching topology (ST) is studied via reinforcement learning. Due to the existence of ST, the kernel matrix of value function is switching-varying, which cannot be applied to existing algorithms. To overcome the inapplicability of varying kernel matrix, a two-layer reinforcement learning algorithm is proposed in this article. To further implement the proposed algorithm, a data-based distributed control policy is presented, which is applicable to both fixed topology and ST. Besides, the proposed method does not need assumptions on the eigenvalues of leader's dynamic matrix, it avoids the assumptions in the previous method. Subsequently, the convergence of algorithm is analyzed. Finally, three simulation examples are provided to verify the proposed algorithm.
本文通过强化学习研究切换拓扑（ST）下离散时间多智能体系统基于数据的输出一致性问题。由于ST的存在，值函数的核矩阵是切换变化的，无法应用于现有算法。为克服可变核矩阵的不可用性，本文提出了一种双层强化学习算法。为进一步实现所提算法，给出了一种基于数据的分布式控制策略，该策略适用于固定拓扑和ST。此外，所提方法不需要对领导者动态矩阵的特征值做假设，避免了先前方法中的假设。随后，分析了算法的收敛性。最后，提供了三个仿真实例来验证所提算法。
