Mathieu Laurière
Mathieu Laurière
Assistant professor of Mathematics and Data Science, NYU Shanghai
Verified email at - Homepage
Cited by
Cited by
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
To appear in Annals of Applied Probability (, 2019
Dynamic programming for mean-field type control
M Laurière, O Pironneau
Comptes Rendus Mathematique 352 (9), 707-713, 2014
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
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning
R Carmona, M Laurière, Z Tan
arXiv preprint arXiv:1910.12802, 2019
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
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
Mean field games and applications: Numerical aspects
Y Achdou, M Laurière
Mean Field Games, 249-307, 2020
Convergence of large population games to mean field games with interaction through the controls
M Laurière, L Tangpi
arXiv preprint arXiv:2004.08351, 2020
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
R Carmona, M Laurière, Z Tan
arXiv preprint arXiv:1910.04295, 2019
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
On the system of partial differential equations arising in mean field type control
Y Achdou, M Lauriere
arXiv preprint arXiv:1503.05044, 2015
Numerical Methods for Mean Field Games and Mean Field Type Control
M Lauriere
arXiv preprint arXiv:2106.06231, 2021
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
On the implementation of a primal-dual algorithm for second order time-dependent mean field games with local couplings
L Briceño-Arias, D Kalise, Z Kobeissi, M Laurière, AM González, FJ Silva
ESAIM: Proceedings and Surveys 65, 330-348, 2019
Multipartite Generalization of Quantum Discord
C Radhakrishnan, M Laurière, T Byrnes
Physical Review Letters 124 (11), 110401, 2020
Quantum Discord and its distribution in multipartite systems
C Radhakrishnan, M Lauriere, T Byrnes
arXiv preprint arXiv:1909.08194, 2019
Mean field control and mean field game models with several populations
A Bensoussan, T Huang, M Laurière
arXiv preprint arXiv:1810.00783, 2018
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
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
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
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