This talk presents recent advances on the design of Machine learning based approximation algorithms for Nash equilibrium in Mean Field games. In particular, a focus will be given on the one using Fictitious Play or Online Mirror Descent iterative schemes. A dedicated open source library, Open Spiel, together with applications to flocking, routing or crowd movements will be presented.