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Cited article:

Recent developments in machine learning methods for stochastic control and games

Ruimeng Hu and Mathieu Laurière
Numerical Algebra, Control and Optimization (2024)
https://doi.org/10.3934/naco.2024031

Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: II—the finite horizon case

René Carmona and Mathieu Laurière
The Annals of Applied Probability 32 (6) (2022)
https://doi.org/10.1214/21-AAP1715

Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Numerical Applications

Achref Bachouch, Côme Huré, Nicolas Langrené and Huyên Pham
Methodology and Computing in Applied Probability 24 (1) 143 (2022)
https://doi.org/10.1007/s11009-019-09767-9

McKean–Vlasov Optimal Control: Limit Theory and Equivalence Between Different Formulations

Mao Fabrice Djete, Dylan Possamaï and Xiaolu Tan
Mathematics of Operations Research 47 (4) 2891 (2022)
https://doi.org/10.1287/moor.2021.1232

Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games I: The Ergodic Case

René Carmona and Mathieu Laurière
SIAM Journal on Numerical Analysis 59 (3) 1455 (2021)
https://doi.org/10.1137/19M1274377