From optimal control to large population games: Learning and Applications

 Organizer(s):
Name:
Affiliation:
Country:
Gokce Dayanikli
University of Illinois Urbana-Champaign
USA
Mathieu Lauriere
New York University Shanghai
Peoples Rep of China
 Introduction:  
  This session explores a broad range of topics, from single-agent optimal control problems to large population games, with a strong emphasis on recent advances in learning-based methods and their practical applications. For large population systems, the presentations will cover developments in mean field game and control theory, including extensions that incorporate heterogeneity through network-based models or diverse agent types. Topics may include graphon games, Stackelberg mean field games, and major-minor mean field frameworks. On the learning side, presentations will involve machine learning and reinforcement learning techniques in both single-agent and multi-agent contexts. The session will also highlight real-world applications of optimal control and mean field game theory in areas such as finance, epidemic control, and energy markets.