most citedPopulation-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning

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cs.LG2025

Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics

Lorenzo Magnino, Kai Shao, Zida Wu +2

Mean field games (MFGs) have emerged as a powerful framework for modeling interactions in large-scale multi-agent systems. Despite recent advancements in reinforcement learning (RL…

cs.LG2025

Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning

Zida Wu, Mathieu Lauriere, Matthieu Geist +2

Mean Field Games (MFGs) offer a powerful framework for studying large-scale multi-agent systems. Yet, learning Nash equilibria in MFGs remains a challenging problem, particularly w…