  Oberman gave a stochastic control formulation of the problem of estimating
the convex envelope of a non-convex function. Based on this, we develop a
reinforcement learning scheme to approximate the convex envelope, using a
variant of Q-learning for controlled optimal stopping. It shows very promising
results on a standard library of test problems.
