Scaling Multi-Agent Reinforcement Learning to the Mean-Field Regime

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• Batu Yardim (ETH Zürich)
• Wednesday 12 November 2025, 14:40-15:20
• Seminar Room 1, Newton Institute.

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SCLW01 - Bridging Stochastic Control And Reinforcement Learning: Theories and Applications

Reinforcement Learning (RL) has achieved remarkable success especially when combined with deep learning, however, scaling RL beyond the single-agent setting remains a major challenge. In particular, the “curse of many agents” hinders the application of RL to systems with thousands or even millions of interacting participants. Such large-scale problems arise naturally in domains like financial markets, auctions, traffic/resource management, and social systems, where optimal decision-making and computation quickly become intractable. We explore mean-field reinforcement learning (MF-RL) as a principled framework to address this challenge under the agent exchangeability assumption. Our work extends the theoretical foundations of MF-RL with an emphasis on computational aspects and realworld applicability. Specifically, we analyze mean-field approximation properties, study communication and coordination bottlenecks during learning, and examine the computational and statistical complexity of scaling RL to the mean-field regime. Finally, we highlight applications to large-scale incentive design and resource allocation, demonstrating how MF-RL can serve as a bridge between mean-field theory and practical multi-agent RL algorithms.

This talk is part of the Isaac Newton Institute Seminar Series series.

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