← Back to all news
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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jun 30

Exploration and Online Transfer with Behavioral Foundation Models

arXiv:2606. 29980v1 Announce Type: new Abstract: Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional learning at transfer time, while training only on reward-free trajectories.

By Louis Bagot (SyCoSMA), Mathieu Lefort (LIRIS, SyCoSMA, IRISA, MALT, UR), La\"etitia Matignon (SyCoSMA)
agentsreinforcement-learning
More like this →
arXiv Machine Learning
Sep 10

Decision-Centered Abstractions via Orthogonal Estimation of Difference-of-Q Functions

The paper introduces state abstractions that preserve the difference of Q‑functions for offline reinforcement learning, aiming to exclude irrelevant dynamics from rich state data. It proposes a dynamic generalization of the R‑learner that uses orthogonal estimation and sparse learning to estimate the Q‑function contrast, achieving faster convergence and consistency under a margin condition. Experiments on simulated and simulator‑augmented real data show variance reductions and demonstrate that the necessary information for sequential decision‑making can be smaller than that required for full state prediction.

By Defu Cao, Angela Zhou
reinforcement-learningsafety
More like this →