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arXiv 2007.11437eess.SYcs.SYmath.OC
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基于极值搜索控制的多智能体动力系统广义Nash平衡点学习

Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control

Suad Krilašević, Sergio Grammatico

摘要:

本文研究强单调对策中广义纳什均衡的学习问题。首先,我们提出了一种新的连续时间求解算法,使用定期投影和一阶信息。作为第二个主要贡献,我们设计了一个数据驱动的前一种算法的变体,每个代理通过零阶信息,即测量他们的个人代价函数值,作为典型的极值寻求控制,估计他们的个人伪梯度。第三,我们推广我们的设置和结果的多智能体系统的非线性动力学。最后,我们将我们的算法应用于机器人传感器网络的连接控制和分布式风电场优化。

英文摘要:

In this paper, we consider the problem of learning a generalized Nash equilibrium (GNE) in strongly monotone games. First, we propose a novel continuous-time solution algorithm that uses regular projections and first-order information. As second main contribution, we design a data-driven variant of the former algorithm where each agent estimates their individual pseudo-gradient via zero-order information, namely, measurements of their individual cost function values, as typical of extremum seeking control. Third, we generalize our setup and results for multi-agent systems with nonlinear dynamics. Finally, we apply our algorithms to connectivity control in robotic sensor networks and distributed wind farm optimization.

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