论文标题
具有应用程序的非本地平均现场游戏的计算方法
Computational methods for nonlocal mean field games with applications
论文作者
论文摘要
我们介绍了一个新颖的框架，以模拟和解决具有非本地相互作用的平均场游戏系统。我们的方法依赖于基于内核的均值相互作用和机器学习中内核方法的功能空间扩展的表示。我们通过对代理之间的各种相互作用方案进行建模来证明我们的方法的灵活性。此外，我们的方法得出了原始问题的计算有效的鞍点重新印度，该重新构造适合最先进的凸优化方法，例如原始二重混合梯度方法（PDHG）。我们还讨论了我们方法在多代理轨迹计划问题上的潜在应用。
We introduce a novel framework to model and solve mean-field game systems with nonlocal interactions. Our approach relies on kernel-based representations of mean-field interactions and feature-space expansions in the spirit of kernel methods in machine learning. We demonstrate the flexibility of our approach by modeling various interaction scenarios between agents. Additionally, our method yields a computationally efficient saddle-point reformulation of the original problem that is amenable to state-of-the-art convex optimization methods such as the primal-dual hybrid gradient method (PDHG). We also discuss potential applications of our methods to multi-agent trajectory planning problems.
