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arXiv Machine Learning By Aditya Makkar, Benjamin Unger, Jeongyeol Kwon, Mathieu Lauri\`ere, Eugene Vinitsky, Yonathan Efroni

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

Read the original on arXiv Machine Learning →

The paper proposes a mean‑field reinforcement learning framework that models rewards and transitions as functions of an unknown low‑dimensional aggregate statistic of a large agent population. By learning this low‑dimensional representation in an offline setting, the authors demonstrate a provable method for obtaining near‑optimal policies. Experiments on a one‑step routing game inspired by supply‑chain problems show that, with a fixed neural‑network size and optimization budget, the learned representation improves reward prediction and the quality of Nash equilibria compared to baselines that ignore population structure.

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