The Rise of Reinforcement Learning: from One to Many

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The Rise of Reinforcement Learning: from One to Many
Speaker: Prof. Niao He
Time: 18:00 (BST), September 25, 2024.
Abstract
Reinforcement learning (RL), combined with deep neural networks, is key to the boom of recent AI breakthroughs from game mastery to control automation. However, their successes are overly reliant on brute-force computing power and engineering tricks, leaving wide gaps between practice and theory. The lack of theoretical foundations is even more pronounced as we shift from single-agent to many-agent RL, in addressing complex dynamic systems and decision making such as resource allocation, traffic management, and social interaction. The challenges inherent in learning many-agent systems stem not only from the increased computational and strategic complexities but also from practical limitations in coordination and exploration. In this talk, I will shed light on promising principles that break the curses of many-agent RL, focusing on mean-field approximation theory, statistical complexity, and independent learning. This will further pave the way for scalable and principled solutions to unlock the full potential of RL for next-generation AI.
Our Speaker
Niao He is an Assistant Professor in the Department of Computer Science at ETH Zurich, where she leads the Optimization and Decision Intelligence (ODI) Group. She is also an ELLIS Scholar and a core faculty member of ETH AI Center, ETH-Max Planck Center of Learning Systems, and ETH Foundations of Data Science. Previously, she was an assistant professor at the University of Illinois at Urbana-Champaign from 2016 to 2020. Before that, she received her Ph.D. degree in Operations Research from Georgia Institute of Technology in 2015. Her research interests lie in large-scale optimization and reinforcement learning, with a primary focus on theoretical and algorithmic foundations for principled, scalable, and trustworthy decision intelligence. She is a recipient of AISTATS Best Paper Award, NSF CRII Award, SNSF Starting Grant, etc.

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