数据多项式增长条件下确定性平均场博弈的逼近

IF 1.1 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Dynamics and Games Pub Date : 2023-01-01 DOI:10.3934/jdg.2023018

Justina Gianatti, Francisco J. Silva, Ahmad Zorkot

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引用次数: 1

摘要

我们考虑了一个确定性的平均场博弈问题，其中一个典型的智能体解决了一个最优控制问题，其中动力学是仿射的，而成本函数是一个多项式增长的状态变量。在此框架下，我们构造了一个离散时间有限状态空间的平均场博弈问题，该问题近似于原博弈的平衡点。给出了用虚拟游戏法求解的两个数值算例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Approximation of deterministic mean field games under polynomial growth conditions on the data

We consider a deterministic mean field game problem in which a typical agent solves an optimal control problem where the dynamics is affine with respect to the control and the cost functional has a growth which is polynomial with respect to the state variable. In this framework, we construct a mean field game problem in discrete time and finite state space that approximates equilibria of the original game. Two numerical examples, solved with the fictitious play method, are presented.

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来源期刊

Journal of Dynamics and Games MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-

CiteScore

2.00

自引率

0.00%

发文量

期刊介绍： The Journal of Dynamics and Games (JDG) is a pure and applied mathematical journal that publishes high quality peer-review and expository papers in all research areas of expertise of its editors. The main focus of JDG is in the interface of Dynamical Systems and Game Theory.