Two-Layer Reinforcement Learning for Output Consensus of Multiagent Systems Under Switching Topology
针对切换拓扑下多智能体系统的输出一致性问题，提出一种双层强化学习算法，无需领导者动态矩阵特征值假设，适用于固定和切换拓扑，并通过仿真验证。
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.