SOURCE-LINKED INTELLIGENCE

Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

Modern multi-agent systems are increasingly deployed at scale over large populations of agents in settings such as ad-auctions, traffic routing, and recommendation systems. The dominant approach in such settings is to optimize each agent's policy independently, treating the other agents as part of a fixed single-agent environment rather than modeling the population dynamics. In many large-population systems, the dynamics depend on an aggregate summary of the population rather than the identity of any individual. Mean-field RL exploits such structure, providing a principled framework that model

Read original source ↗ Open in workspace

recordType

paper

region

Global