We propose machine learning methods for solving fully nonlinear partial differential equations (PDEs) with convex Hamiltonian. Our algorithms are conducted in two steps. First, the PDE is rewritten its dual stochastic control representation form, and corresponding optimal feedback estimated using a neural network. Next, three different presented to approximate associated value function, i.e., s...