Seuraa
Mathieu Laurière
Mathieu Laurière
Assistant professor of Mathematics and Data Science, NYU Shanghai
Vahvistettu sähköpostiosoite verkkotunnuksessa nyu.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: II—the finite horizon case
R Carmona, M Laurière
The Annals of Applied Probability 32 (6), 4065-4105, 2022
1592022
Fictitious play for mean field games: Continuous time analysis and applications
S Perrin, J Pérolat, M Laurière, M Geist, R Elie, O Pietquin
Advances in Neural Information Processing Systems 33, 2020
1432020
Model-free mean-field reinforcement learning: mean-field MDP and mean-field Q-learning
R Carmona, M Laurière, Z Tan
The Annals of Applied Probability 33 (6B), 5334-5381, 2023
1282023
Mean field games and applications: Numerical aspects
Y Achdou, M Laurière
Mean Field Games, 249-307, 2020
1162020
On the convergence of model free learning in mean field games
R Elie, J Perolat, M Laurière, M Geist, O Pietquin
Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7143-7150, 2020
1122020
Dynamic programming for mean-field type control
M Laurière, O Pironneau
Comptes Rendus Mathematique 352 (9), 707-713, 2014
1102014
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games I: The Ergodic Case
R Carmona, M Laurière
SIAM Journal on Numerical Analysis 59 (3), 1455-1485, 2021
1072021
COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability
A Charpentier, R Elie, M Laurière, VC Tran
Mathematical Modelling of Natural Phenomena 15, 57, 2020
972020
Optimal incentives to mitigate epidemics: a Stackelberg mean field game approach
A Aurell, R Carmona, G Dayanikli, M Lauriere
SIAM Journal on Control and Optimization 60 (2), S294-S322, 2022
962022
Numerical methods for mean field games and mean field type control
M Lauriere
Mean field games 78 (221-282), 2021
852021
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems
A Angiuli, JP Fouque, M Laurière
Mathematics of Control, Signals, and Systems (arXiv preprint arXiv:2006.13912), 2020
832020
Scaling up Mean Field Games with Online Mirror Descent
J Perolat, S Perrin, R Elie, M Laurière, G Piliouras, M Geist, K Tuyls, ...
AAMAS 2022 (arXiv preprint arXiv:2103.00623), 2021
782021
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
R Carmona, M Laurière, Z Tan
arXiv preprint arXiv:1910.04295, 2019
782019
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
M Geist, J Pérolat, M Laurière, R Elie, S Perrin, O Bachem, R Munos, ...
AAMAS 2022 (arXiv preprint arXiv:2106.03787), 2021
732021
Stochastic Graphon Games: I. The Static Case
R Carmona, D Cooney, C Graves, M Lauriere
Mathematics of Operations Research (arXiv preprint arXiv:1911.10664), 2019
712019
On the system of partial differential equations arising in mean field type control
Y Achdou, M Lauriere
Discrete and Continuous Dynamical Systems 35 (9), 3879-3900, 2015
692015
Convergence of large population games to mean field games with interaction through the controls
M Laurière, L Tangpi
SIAM Journal on Mathematical Analysis 54 (3), 3535-3574, 2022
662022
Learning Mean Field Games: A Survey
M Laurière, S Perrin, M Geist, O Pietquin
arXiv preprint arXiv:2205.12944, 2022
632022
Scalable deep reinforcement learning algorithms for mean field games
M Lauriere, S Perrin, S Girgin, P Muller, A Jain, T Cabannes, G Piliouras, ...
International Conference on Machine Learning, 12078-12095, 2022
622022
Mean field control and mean field game models with several populations
A Bensoussan, T Huang, M Laurière
arXiv preprint arXiv:1810.00783, 2018
622018
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Artikkelit 1–20