arXiv Machine Learning By Wenhao Li, Xiangfeng Wang, Bo Jin

Mean-Field Diffuser: Scaling Offline MARL to Thousands of Agents

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arXiv:2605. 30190v2 Announce Type: replace Abstract: Diffusion-based planning has achieved strong results in single-agent offline reinforcement learning, yet scaling to many-agent systems remains intractable due to the curse of dimensionality in the joint trajectory space.

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arXiv Machine Learning
Sep 4

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

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.

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