# Learning Deep Mean Field Games for Modeling Large Population Behavior

arXiv: https://arxiv.org/abs/1711.03156 · HTML: https://mlsanity.com/paper/1711.03156/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2017.

## Built on by (most cited first, 8 of 8)

- [Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint](https://mlsanity.com/paper/2106.03787/) (2021)
- [Reinforcement Learning for Mean Field Games, with Applications to Economics](https://mlsanity.com/paper/2106.13755/) (2021)
- [Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networks](https://mlsanity.com/paper/2312.09953/) (2023)
- [Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm](https://mlsanity.com/paper/2405.08005/) (2024)
- [Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL](https://mlsanity.com/paper/2401.06566/) (2024)
- [Kernel Based Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games](https://mlsanity.com/paper/2507.14529/) (2025)
- [Towards Inverse Reinforcement Learning for Limit Order Book Dynamics](https://mlsanity.com/paper/1906.04813/) (2019)
- [Scalable Offline Reinforcement Learning for Mean Field Games](https://mlsanity.com/paper/2410.17898/) (2024)

## Abstract (arXiv)

We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning. Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.