# Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables

arXiv: https://arxiv.org/abs/2509.03845 · HTML: https://mlsanity.com/paper/2509.03845/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning](https://mlsanity.com/paper/2102.01585/) (2021)
- [Multi-Agent Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1907.13220/) (2019)
- [Meta-Inverse Reinforcement Learning with Probabilistic Context Variables](https://mlsanity.com/paper/1909.09314/) (2019)
- [On the Convergence of Model Free Learning in Mean Field Games](https://mlsanity.com/paper/1907.02633/) (2019)
- [Learning in Mean Field Games: the Fictitious Play](https://mlsanity.com/paper/1507.06280/) (2015)
- [Learning a Prior over Intent via Meta-Inverse Reinforcement Learning](https://mlsanity.com/paper/1805.12573/) (2018)
- [Multi Type Mean Field Reinforcement Learning](https://mlsanity.com/paper/2002.02513/) (2020)
- [A Simple Neural Attentive Meta-Learner](https://mlsanity.com/paper/1707.03141/) (2017)
- [The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models](https://mlsanity.com/paper/1806.06514/) (2018)
- [Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents](https://mlsanity.com/paper/2012.15377/) (2020)

## Built on by (most cited first, 3 of 3)

- [Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm](https://mlsanity.com/paper/2509.23135/) (2025)
- [Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning](https://mlsanity.com/paper/2607.08647/) (2026)
- [Population-Aware Imitation Learning in Mean-field Games with Common Noise](https://mlsanity.com/paper/2605.03357/) (2026)

## Abstract (arXiv)

Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations. While promising, the assumption of agent homogeneity limits the capability of existing methods to handle demonstrations with heterogeneous and unknown objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method. Critically, our method can infer rewards from different yet structurally similar tasks without prior knowledge about underlying contexts or modifying the MFG model itself. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs.