# Scaling up Mean Field Games with Online Mirror Descent

*Source* · `src-perolat-2021-mfg-omd` · status: **n/a**

| field | value |
|---|---|
| kind | Preprint |
| authors | Julien Perolat, Sarah Perrin, Romuald Elie, Mathieu Lauriere, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin |
| date | 2021-02-28 |
| url | https://arxiv.org/abs/2103.00623 |
| arxiv | 2103.00623 |
| accessed | 2026-10-24 |
| notes | Online mirror descent converges to MFG Nash equilibrium under monotonicity; faster than fictitious play; solves MFGs with hundreds of billions of states. |

## Backlinks (computed)

**cited by:** [Mean field games and mean-field learning](../technique/mean-field-games.md)

## Citation

Julien Perolat, Sarah Perrin, Romuald Elie, Mathieu Lauriere, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin (2021-02-28). *Scaling up Mean Field Games with Online Mirror Descent*.  https://arxiv.org/abs/2103.00623

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Provenance: created in run `2026-10-24-techniques-sim-math`.