Hugging Face Trending Papers
Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games
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arXiv Machine Learning
Oct 8
Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games
arXiv:2610.09244v1 Announce Type: cross Abstract: Recent advances in mean-field games and its multi-population variants enable large-scale heterogeneous multi-agent systems to be modeled through repr...
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.
arXiv Machine Learning
Jun 19
Optimal Coarse Correlated Equilibria in Mean Field Games: Linear Programming and No-Regret Learning
arXiv:2606. 20062v1 Announce Type: cross Abstract: We introduce optimal coarse correlated equilibria for continuous-time mean field games.
arXiv Machine Learning
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Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games with Average Reward
arXiv:2606. 16759v1 Announce Type: new Abstract: We study inverse reinforcement learning for discrete-time, infinite-horizon mean-field games (MFGs) under an average-reward criterion.
arXiv Machine Learning
Jun 9
Population-Aware Imitation Learning in Mean-field Games with Common Noise
arXiv:2605. 03357v2 Announce Type: replace Abstract: Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents.
arXiv Machine Learning
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Regret, equilibrium, and learning in games: A guided tour
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.