Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations
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 models each agent's environment as an explicit function of the population distribution. However, in la...
| Authors | Aditya Makkar, Benjamin Unger, Jeongyeol Kwon et al. |
|---|---|
| Primary category | cs.MA |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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