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.
