Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Recent advances in mean-field games and its multi-population variants enable large-scale heterogeneous multi-agent systems to be modeled through representative agents and their associated mean-field d...
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:2606. 20062v1 Announce Type: cross Abstract: We introduce optimal coarse correlated equilibria for continuous-time mean field games.
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: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.
The paper studies repeated principal-agent games in a multi-armed bandit setting where the principal offers arm-specific incentives, the agent chooses an arm to maximize its own reward plus incentive, and the principal observes only the chosen arm and its own reward. Because the principal cannot see the agent’s reward, traditional estimation fails, so the authors develop an estimator based on the sequence of incentives and chosen arms, analogous to online inverse optimization. Using this estimator, they design a policy that achieves low (square‑root) regret and validate the approach with simulations for personalized incentive design in long‑term statin treatment.