Publications

Mathieu Laurière, Sarah Perrin, Matthieu Geist, Olivier Pietquin (2022). Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. Preprint.

Mathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist (2022). Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. ICML 2022.

Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Elie, Matthieu Geist, Olivier Pietquin (2021). Generalization in Mean Field Games by Learning Master Policies. AAAI 2022.

Theophile Cabannes, Mathieu Laurière, Julien Pérolat, Raphael Marinier, Sertan Girgin, Sarah Perrin, Olivier Pietquin, Alexandre M Bayen, Eric Goubault, Romuald Elie (2021). Scalable Deep Reinforcement Learning Algorithms for Mean Field Games.

Matthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin (2021). Concave Utility Reinforcement Learning: the Mean-field Game viewpoint. Under review.

Julien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin (2021). Scaling up Mean Field Games with Online Mirror Descent. Under review.

Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Elie, Olivier Pietquin (2021). Mean Field Games Flock! The Reinforcement Learning Way. IJCAI 2021.

Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin (2020). Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. NeurIPS 2020.

Sarah Perrin, Thierry Roncalli (2019). Machine learning optimization algorithms & portfolio allocation. Machine Learning for Asset Management: New Developments and Financial Applications.