An Effective Gradient Projection Method for Stochastic Optimal Control

Abstract

In this work, we propose a simple yet effective gradient projection algorithm for a class of stochastic optimal control problems. The basic iteration block is to compute gradient projection of the objective functional by solving the state and co-state equations via some Euler methods and by using the Monte Carlo simulations. Convergence properties are discussed and extensive numerical tests are carried out. Possibility of extending this algorithm to more general stochastic optimal control is also discussed.