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Spring 2022 GRASP SFI: Scott Guan, Georgia Institute of Technology, “Toward Scalability in Multi-Agent Decision Making”

March 23 @ 3:00 pm - 4:00 pm

*This was a HYBRID Event with in-person attendance in Levine 512 and Virtual attendance…

ABSTRACT

While machine learning algorithms have led to tremendous improvements in many multi-agent domains, scalability remains one of the major challenges for multi-agent decision-making. In this talk, we will focus on two aspects of the scalability challenge: (i) number of agents, and (ii) large state space. We will propose possible approaches to remedy both challenges. In the first part, we introduce the mean-field approximation, which simplifies the interactions among a large population of agents. We will present theoretical analysis and convergence results on a class of entropy-regularized mean-field games with optimality bounds. In the second part, we address the large state space issue using two ideas: first, the use of hierarchical decomposition to decompose the original game to a number of smaller games; and second, the approximation of expensive operators (e.g., minimax) to reduce computation time in multi-agent reinforcement learning. Convergence analysis and application to pursuit-evasion games will also be discussed.

Presenter

Scott Guan

Scott Guan

Yue Guan (Scott) is a Ph.D. candidate in the School of Aerospace Engineering at Georgia Tech, advised by Panagiotis Tsiotras. He received a B.S. in Aerospace Engineering from Georgia Tech. His research interest lies in machine learning, game theory and optimal control, with his PhD work primarily focused on multi-agent decision-making.

Details

Date:
March 23
Time:
3:00 pm - 4:00 pm
Event Category:

Venue

Levine 512
3330 Walnut Street
Philadelphia,
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