From optimal control to large population games: Learning and Applications
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Gokce Dayanikli
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University of Illinois Urbana-Champaign
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USA
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Mathieu Lauriere
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New York University Shanghai
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Peoples Rep of China
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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.
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