  We propose several algorithms to solve McKean-Vlasov Forward Backward
Stochastic Differential Equations. Our schemes rely on the approximating power
of neural networks to estimate the solution or its gradient through
minimization problems. As a consequence, we obtain methods able to tackle both
mean field games and mean field control problems in moderate dimension. We
analyze the numerical behavior of our algorithms on several examples including
non linear quadratic models.
