mlsanityData to 7 Oct 2026

Learning in anonymous nonatomic games with applications to first-order mean field games

arXiv 1704.00378 ↗First posted 2 Apr 2017math.OCcited by 10

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Abstract (arXiv)

We introduce a model of anonymous games with the player dependent action sets. We propose several learning procedures based on the well-known Fictitious Play and Online Mirror Descent and prove their convergence to equilibrium under the classical monotonicity condition. Typical examples are first-order mean field games.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2017.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 2; 2025: 1.

10 papers.

  1. 2022Scalable Deep Reinforcement Learning Algorithms for Mean Field Gamescited by 33
  2. 2021Scaling up Mean Field Games with Online Mirror Descentcited by 29
  3. 2021Generalization in Mean Field Games by Learning Master Policiescited by 28
  4. 2021Reinforcement Learning for Mean Field Games, with Applications to Economicscited by 14
  5. 2022Learning Sparse Graphon Mean Field Gamescited by 14
  6. 2024Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learningcited by 5
  7. 2023Learning Regularized Graphon Mean-Field Games with Unknown Graphonscited by 4
  8. 2025Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learningcited by 4
  9. 2021Exploration noise for learning linear-quadratic mean field gamescited by 3
  10. 2024Networked Communication for Mean-Field Games with Function Approximation and Empirical Mean-Field Estimationcited by 0