论文标题
通过超级寻求控制，在多代理动力学系统中学习通用的NASH平衡
Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control
论文作者
论文摘要
在本文中，我们考虑了在强烈单调游戏中学习通用纳什均衡（GNE）的问题。首先，我们提出了一种新型的连续时间解决方案算法，该算法使用常规预测和一阶信息。作为第二个主要贡献，我们设计了以前算法的数据驱动变体，其中每个代理通过零级信息估算其单个伪级奖学金，即，测量其个体成本函数值，是超级寻求控制的典型代理。第三，我们将设置和结果概括为具有非线性动力学的多代理系统。最后，我们将算法应用于机器人传感器网络和分布式风电场优化的连接控制。
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.
