标题： 多智能体系统中学习最优交互权重 显示英文标题

标题： Learning Optimal Interaction Weights in Multi-Agents Systems

摘要： 本文提出了一种时空逆最优控制框架，用于理解多智能体系统（MAS）中的交互。 我们采用图表示方法，并将代理之间交互的动力学建模为一致性算法中状态相关的边权重，同时考虑空间和时间动力学。 我们的方法从轨迹观察中学习这些边权重，例如由专家演示提供的数据，这使我们能够捕捉非线性、分布式交互行为的复杂性。 我们推导了这些交互权重最优性的充要条件，解释了网络拓扑如何影响MAS的协调。 所提出的方法在一个多智能体编队控制问题中得到验证，在该问题中，我们展示了它从样本轨迹数据中恢复交互权重和协调模式的有效性。 显示英文摘要

摘要： This paper presents a spatio-temporal inverse optimal control framework for understanding interactions in multi-agent systems (MAS). We employ a graph representation approach and model the dynamics of interactions between agents as state-dependent edge weights in a consensus algorithm, incorporating both spatial and temporal dynamics. Our method learns these edge weights from trajectory observations, such as provided by expert demonstrations, which allows us to capture the complexity of nonlinear, distributed interaction behaviors. We derive necessary and sufficient conditions for the optimality of these interaction weights, explaining how the network topology affects MAS coordination. The proposed method is demonstrated on a multi-agent formation control problem, where we show its effectiveness in recovering the interaction weights and coordination patterns from sample trajectory data.