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arXiv 2408.09652math.OC
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部分观测随机系统的线性二次平均场博弈

Linear-Quadratic Mean-Field Game for Stochastic Systems with Partial Observation

Min Li, Na Li, Zhen Wu

AI总结:

针对部分信息下的线性二次随机大群体耦合控制问题,结合平均场博弈与后向分离等方法推导分散最优控制的开环与滤波反馈形式,验证ε-Nash均衡性并给出金融算例。

摘要:

本文研究一类带部分信息的线性二次随机大群体问题,其中单个智能体仅能获取与状态相关的含噪观测过程。每个智能体的动力学服从由个体噪声驱动的线性随机微分方程,且所有智能体通过控制平均项耦合在一起。借助平均场博弈方法以及结合状态分解技术的后向分离原理,可通过带条件期望的正倒向随机微分方程,得到开环形式的分散最优控制,同时给出了最优滤波方程。通过解耦方法,分散最优控制还可进一步表示为基于Riccati方程的状态滤波反馈形式。文中给出了控制平均极限的显式解,并讨论了一致性条件系统,此外还验证了相关的ε-Nash均衡性质。为说明理论结果的良好性能,本文研究了一个金融领域的算例。

英文摘要:

This paper is concerned with a class of linear-quadratic stochastic large-population problems with partial information, where the individual agent only has access to a noisy observation process related to the state. The dynamics of each agent follows a linear stochastic differential equation driven by individual noise, and all agents are coupled together via the control average term. Using the mean-field game approach and the backward separation principle with a state decomposition technique, the decentralized optimal control can be obtained in the open-loop form through a forward-backward stochastic differential equation with the conditional expectation. The optimal filtering equation is also provided. By the decoupling method, the decentralized optimal control can also be further presented as the feedback of state filtering via the Riccati equation. The explicit solution of the control average limit is given, and the consistency condition system is discussed. Moreover, the related $\varepsilon$-Nash equilibrium property is verified. To illustrate the good performance of theoretical results, an example in finance is studied.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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