  In this paper, we mainly focus on the numerical solution of high-dimensional
stochastic optimal control problem driven by fully-coupled forward-backward
stochastic differential equations (FBSDEs in short) through deep learning. We
first transform the problem into a stochastic Stackelberg differential
game(leader-follower problem), then a cross-optimization method (CO method) is
developed where the leader's cost functional and the follower's cost functional
are optimized alternatively via deep neural networks. As for the numerical
results, we compute two examples of the investment-consumption problem solved
through stochastic recursive utility models, and the results of both examples
demonstrate the effectiveness of our proposed algorithm.
