F. Bach, Breaking the curse of dimensionality with convex neural networks, Journal of Machine Learning Research, vol.18, issue.19, pp.1-53, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01098505

A. Bachouch, C. Huré, N. Langrené, and H. Pham, Deep neural networks algorithms for stochastic control problems on finite horizon, part II: numerical applications, 2018.

A. Balata and J. Palczewski, Regress-later Monte-Carlo for optimal inventory control with applications in energy, 2018.

D. P. Bertsekas and J. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, 1996.

G. Cybenko, Approximations by superpositions of sigmoidal functions. Mathematics of Control, Signals, and Systems, vol.2, pp.303-314, 1989.

E. Weinan, J. Han, and A. Jentzen, Deep learning-based numerical methods for highdimensional parabolic partial differential equations and backward stochastic differential equations, Communications in Mathematics and Statistics, vol.5, pp.349-380, 2017.

A. Géron, Deep Learning avec TensorFlow. O'Reilly Media, 2017.

P. Glasserman and B. Yu, Simulation for American options: regression now or regression later? Monte Carlo and Quasi-Monte Carlo Methods, pp.213-226, 2004.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, 2016.

S. Graf and H. Luschgy, Foundations of Quantization for Probability Distributions, vol.1730, 2000.

J. Guyon and P. Henry-labordere, Uncertain volatility model: a Monte-Carlo approach, 2010.

L. Györfi, M. Kohler, A. Krzyzak, and H. Walk, A Distribution-Free Theory of Nonparametric Regression, Springer Series in Statistics, 2002.

J. Han and E. Weinan, Deep learning approximation for stochastic control problems, 2016.

J. Han and J. Long, Convergence of the deep BSDE method for coupled FBSDEs, 2018.

P. Henry-labordere, Deep primal-dual algorithm for BSDEs: Applications of machine learning to CVA and IM, p.3071506, 2017.

K. Hornick, Approximation capabilities of multilayer feedforward networks, Neural Networks, vol.4, pp.251-257, 1991.

I. Kharroubi, N. Langrené, and H. Pham, A numerical algorithm for fully nonlinear HJB equations: an approach by control randomization, Monte Carlo Methods and Applications, vol.20, issue.2, pp.145-165, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01019472

M. Kohler, Nonparametric regression with additional measurement errors in the dependent variable, Journal of Statistical Planning and Inference, vol.136, issue.10, pp.3339-3361, 2006.

M. Kohler, A. Krzy?-zak, and N. Todorovic, Pricing of high-dimensional American options by neural networks, Mathematical Finance, vol.20, issue.3, pp.383-410, 2010.

A. N. Kolmogorov, On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables, Mathematics and Its Applications (Soviet Series), vol.25, 1991.

S. Kou, X. Peng, and X. Xu, EM algorithm and stochastic control. Available at SSRN, 2016.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, pp.436-444, 2015.

Y. Li, Deep reinforcement learning: an overview. arXiv 1701.07274v3, 2017.

A. Francis, E. S. Longstaff, and . Schwartz, Valuing American options by simulation: A simple least-squares approach, The Review of Financial Studies, vol.14, issue.1, pp.113-147, 2001.

M. Ludkovski and A. Maheshwari, Simulation methods for stochastic storage problems: A statistical learning perspective, 2018.

V. Mnih, K. Kavukcuoglu, D. Silver, and A. A. Rusu, Human-level control through deep reinforcement learning, Nature, vol.518, pp.529-533, 2015.

M. Nielsen, Neural networks and deep learning

G. Pagès, H. Pham, and J. Printems, Optimal quantization methods and applications to numerical problems in finance. Handbook of computational and numerical methods in finance, pp.253-297, 2004.

W. B. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2011.

R. S. Sutton, A. G. Barto, and . Learning, , 1998.

C. Ashia, R. Wilson, M. Roelofs, N. Stern, B. Srebro et al., The marginal value of adaptive gradient methods in machine learning, 31st Conference on Neural Information Processing Systems, 2017.