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

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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.