# Correlated equilibria and mean field games: a simple model

arXiv: https://arxiv.org/abs/2004.06185 · HTML: https://mlsanity.com/paper/2004.06185/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Learning Correlated Equilibria in Mean-Field Games](https://mlsanity.com/paper/2208.10138/) (2022)
- [Major-Minor Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2303.10665/) (2023)
- [Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control](https://mlsanity.com/paper/2209.07420/) (2022)
- [Mean Field Correlated Imitation Learning](https://mlsanity.com/paper/2404.09324/) (2024)
- [Bounded Rationality Equilibrium Learning in Mean Field Games](https://mlsanity.com/paper/2411.07099/) (2024)
- [The Learning Approach to Games](https://mlsanity.com/paper/2503.00227/) (2025)

## Abstract (arXiv)

In the context of simple finite-state discrete time systems, we introduce a generalization of mean field game solution, called correlated solution, which can be seen as the mean field game analogue of a correlated equilibrium. Our notion of solution is justified in two ways: We prove that correlated solutions arise as limits of exchangeable correlated equilibria in restricted (Markov open-loop) strategies for the underlying N-player games, and we show how to construct approximate N-player correlated equilibria starting from a correlated solution to the mean field game.