Mean-Field Games & Mean-Field Control Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

When the number of agents grows large, interactions are captured by the distribution of the population itself. This list covers the Lasry–Lions and Huang–Malhame–Caines foundations and the modern learning algorithms that scale them.

Mean-Field Games & Mean-Field Control: 10 key papers

1. Learning in Mean Field Games: A Survey
   Laurière et al. arXiv 2022.
2. Q-Learning in Regularized Mean-field Games
   Anahtarci et al. arXiv 2020.
3. Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
   Perrin et al. arXiv 2020.
4. Scaling up Mean Field Games with Online Mirror Descent
   Perolat et al. arXiv 2021.
5. Mean Field Multi-Agent Reinforcement Learning
   Yang et al. arXiv 2018.
6. Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
   Cui and Koeppl. arXiv 2021.
7. Reinforcement Learning in Stationary Mean-field Games
   Subramanian and Mahajan. 2019.
8. Learning Mean-Field Games
   Guo et al. arXiv 2019.
9. Unified Reinforcement Q-Learning for Mean Field Game and Control Problems
   Angiuli et al. arXiv 2020.
10. Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
    Geist et al. arXiv 2021.

Related reading lists

• Game Theory for Multi-Agent Systems
• Self-Play & Population-Based Training
• Population-Based Training
• Opponent Modeling & Theory of Mind
• Kernel Methods & RKHS
• Variational Inference & ELBO
• Multi-Agent Reinforcement Learning
• Robust & Risk-Sensitive RL