Mean field games and machine learning in distributed systems

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dc.contributor.advisorHan, Zhu
dc.contributor.committeeMemberPan, Miao
dc.contributor.committeeMemberChen, Jiefu
dc.contributor.committeeMemberMayerich, David
dc.contributor.committeeMemberLi, Wuchen
dc.creatorGAO, HAO
dc.creator.orcid0000-0002-0505-3676
dc.date.accessioned2023-07-09T02:03:41Z
dc.date.createdMay 2023
dc.date.issued2023-05-12
dc.date.updated2023-07-09T02:03:41Z
dc.description.abstractMean field game (MFG) theory is a game-theoretic framework studying the decision-making of a large number of indistinguishable, rational, and heterogeneous agents. The differential game for a large number of agents are typically untractable due to the complex interactions between them. MFG reduces the differential game to an optimal control problem, where a generic user reacts to the mean effect of other players (mean field) instead of separately dealing with the influence of them. A reference player makes the optimal decision based on the Hamilton-Jacobi-Bellman (HJB) equation. Then the mean field evolves based on the Fokker-Planck-Kolmogorov (FPK) equation. However, when the dimension of the state increases, the computational complexity of traditional numerical methods grows exponentially due to the curse of dimensionality. In this dissertation, there are three main contributions in the field of MFG and Machine Learning. First, we have explored the applications and computational methods for MFG with both continuous and discrete state. In particular, our research starts with the discrete FPK on graph, which is a well-defined gradient flow to describe the evolution of the population given a utility function. After exploring the MFG in a discrete state space, we begin to work on MFG in continuous state and time, consisting of a PDE system of FPK and HJB. Proximal dual hybrid gradient (PDHG) is the numerical method used to solve the MFG in the low dimension. Second, we have extended the MFG to high dimensions by developing a generative adversarial networks-based method, which can efficiently solve stochastic MFGs up to 100 dimensions. Finally, we have applied MFGs to optimize traditional machine learning frameworks. For example, MFG can be used to compute the target action in the deep reinforcement learning, leading to a faster and stable convergence. MFG is also used to compute the device selection probability in the hierarchical federated learning. MFGs have been applied to many different real-world scenarios. For instance, we have developed real-time resource allocation algorithm for the multi-access edge computing systems, the trajectory optimization method of swarm of unmanned aerial vehicles, and the driving range estimation framework of battery electric vehicles. Beyond that, during the outbreak of COVID-19, we have made contributions to designing transmission models and opinion diffusion models in social network.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Gao, Hao, Wuchen Li, Reginald A. Banez, Zhu Han, and H. Vincent Poor. "Mean field evolutionary dynamics in dense-user multi-access edge computing systems." IEEE Transactions on Wireless Communications 19, no. 12 (2020): 7825-7835; and in: Gao, Hao, Wonjun Lee, Yuhan Kang, Wuchen Li, Zhu Han, Stanley Osher, and H. Vincent Poor. "Energy-efficient velocity control for massive numbers of uavs: A mean field game approach." IEEE Transactions on Vehicular Technology 71, no. 6 (2022): 6266-6278; and in: Gao, Hao, Alex Lin, Reginald A. Banez, Wuchen Li, Zhu Han, Stanley Osher, and H. Vincent Poor. "Opinion Evolution in Social Networks: Connecting Mean Field Games to Generative Adversarial Nets." IEEE Transactions on Network Science and Engineering 9, no. 4 (2022): 2734-2746; and in: Shi, Dian, Hao Gao, Li Wang, Miao Pan, Zhu Han, and H. Vincent Poor. "Mean field game guided deep reinforcement learning for task placement in cooperative multiaccess edge computing." IEEE Internet of Things Journal 7, no. 10 (2020): 9330-9340.
dc.identifier.urihttps://hdl.handle.net/10657/14934
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMean field game
dc.subjectFokker-Planck-Kolmogorov equation
dc.subjectHamilton-Jacobi-Bellman equation
dc.subjectMachine learning
dc.subjectGenerative adversarial networks
dc.subjectReinforcement learning
dc.subjectFederated learning
dc.titleMean field games and machine learning in distributed systems
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2025-05-01
local.embargo.terms2025-05-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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