CAREER: Toward Hierarchical Game Theory and Hybrid Learning Framework for Safe, Efficient Large-scale Multi-agent Systems

职业：面向安全、高效的大规模多智能体系统的分层博弈论和混合学习框架

基本信息

批准号：
2144646
负责人：
Hao Xu
金额：
$ 50.48万
依托单位：
Board of Regents, NSHE, obo University of Nevada, Reno
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2027-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144646&HistoricalAwards=false
关键词：
CAREER Toward Hierarchical Game Theory

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Large scale multi-agent systems (LS-MAS), such as wide area power management systems, smart transportation, ultra-dense network in 5G/6G, and so on, are transforming our world rapidly. Before harvesting the benefits from those LS-MAS, it is necessary to develop a feasible methodology that can enhance the efficiency and resiliency of LS-MAS in real-time even under uncertainties and disturbances. Although existing game theory, artificial intelligence (AI), and machine learning (ML) achievement in multi-agent systems optimization are exciting, there is still a gap for applying those theories and techniques to LS-MAS since a large number of agents will cause the intractable computational complexity in both optimization and learning, well-known as “curse-of-dimensionality”. This project aims to investigate the new theory along with efficient and feasible AI/ML approaches that cannot only balance the LS-MAS optimality efficiency and computational complexity theoretically but also learn the LS-MAS optimal solution in real-time with resilience guaranteed. The research is complemented by the integration of research and education plan including a two-way education/research pipeline between UNR and PVAMU (a renowned HBCU). Through the annual summer camp and graduate student joint-research program, UNR and PVAMU can exchange students and faculties especially from underrepresented groups to increase diversity. Through industrial-university interaction, this project also plans to translate outcomes to practice and boost Nevada’s (EPSCoR state) economy. The goal of this project is to advance foundational knowledge of game theory and scientific methodologies of data-enabled learning for enhancing the resiliency and efficiency in large scale multi-agent systems (LS-MAS). Due to ultra large number of agents, it is very challenging to balance the computational complexity and optimal efficiency in LS-MAS. To overcome this challenge, this project will provide several novel contributions, including i) A novel hierarchical game theory (HGT) that can maintain the LS-MAS optimal efficiency while simultaneously balancing computational complexity, ii) A new type of backward stochastic differential equation based actor-critic reinforcement learning to solve the high-dimensional HGT-based LS-MAS optimization problem, and iii) A quality-of-performance driven reliable, efficient, safe hybrid reinforcement learning framework that can balance learning efficiency and computational complexity with safety guaranteed and further pave the way to real-time learning-based LS-MAS optimization even with uncertainties from harsh environments. This project will lead a new direction in machine learning, optimal control, and game theory in real-time LS-MAS optimization, and also contribute to a variety of emerging LS-MAS, e.g. smart transportation, wide area power management systems, etc., which are of national priority.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。大规模多智能体系统 (LS-MAS)，例如广域电力管理系统、智能交通、超5G/6G 等密集网络正在迅速改变我们的世界，在从 LS-MAS 中获益之前，有必要开发一种可行的方法来提高网络的效率和弹性。尽管现有的博弈论、人工智能（AI）和机器学习（ML）在多智能体系统优化方面的成就令人兴奋，但应用这些理论和技术仍然存在差距。由于大量智能体会导致优化和学习中难以处理的计算复杂性，即众所周知的“维数灾难”，该项目旨在研究新的理论以及高效可行的人工智能/。 ML 方法不仅可以平衡理论上 LS-MAS 最优效率和计算复杂性，但也实时学习 LS-MAS 最优解，并保证弹性该研究得到研究和教育计划的整合的补充，包括 UNR 和 UNR 之间的双向教育/研究管道。 PVAMU（著名的 HBCU）。通过每年一度的夏令营和研究生联合研究计划，UNR 和 PVAMU 可以交换学生和教师，特别是来自代表性不足的群体，以增加多样性。还计划将成果转化为实践并促进内华达州（EPSCoR 州）的经济该项目的目标是推进博弈论和数据支持学习的科学方法的基础知识，以提高大规模多智能体系统的弹性和效率。（LS-MAS）。由于代理数量过多，平衡 LS-MAS 的计算复杂性和最佳效率非常具有挑战性，该项目将提供一些新颖的贡献，包括 i）一种新颖的分层结构。博弈论(HGT)，可以保持 LS-MAS 最优效率，同时平衡计算复杂性，ii) 一种新型的基于后向随机微分方程的行动者批评家强化学习，用于解决基于高维 HGT 的 LS-MAS 优化问题，以及iii) 一个性能质量驱动的可靠、高效、安全的混合强化学习框架，可以平衡学习效率和计算复杂性与安全性，并进一步为基于实时学习的 LS-MAS 优化铺平道路，即使在严酷的环境保证不确定性的情况下环境。项目将引领实时LS-MAS优化中机器学习、控制和博弈论的新的优化方向，也有助于各种新兴的LS-MAS，例如智能交通、广域电力管理系统等，该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。