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

arXiv: https://arxiv.org/abs/1704.00378 · HTML: https://mlsanity.com/paper/1704.00378/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2017.

## Built on by (most cited first, 11 of 11)

- [Scalable Deep Reinforcement Learning Algorithms for Mean Field Games](https://mlsanity.com/paper/2203.11973/) (2022)
- [Scaling up Mean Field Games with Online Mirror Descent](https://mlsanity.com/paper/2103.00623/) (2021)
- [Generalization in Mean Field Games by Learning Master Policies](https://mlsanity.com/paper/2109.09717/) (2021)
- [Reinforcement Learning for Mean Field Games, with Applications to Economics](https://mlsanity.com/paper/2106.13755/) (2021)
- [Learning Sparse Graphon Mean Field Games](https://mlsanity.com/paper/2209.03880/) (2022)
- [Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning](https://mlsanity.com/paper/2403.03552/) (2024)
- [Learning Regularized Graphon Mean-Field Games with Unknown Graphons](https://mlsanity.com/paper/2310.17531/) (2023)
- [Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning](https://mlsanity.com/paper/2509.03030/) (2025)
- [Exploration noise for learning linear-quadratic mean field games](https://mlsanity.com/paper/2107.00839/) (2021)
- [Networked Communication for Mean-Field Games with Function Approximation and Empirical Mean-Field Estimation](https://mlsanity.com/paper/2408.11607/) (2024)
- [Optimal Coarse Correlated Equilibria in Mean Field Games: Linear Programming and No-Regret Learning](https://mlsanity.com/paper/2606.20062/) (2026)

## 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.