  This paper studies the stochastic optimal control problem for systems with
unknown dynamics. A novel decoupled data based control (D2C) approach is
proposed, which solves the problem in a decoupled "open loop-closed loop"
fashion that is shown to be near-optimal. First, an open-loop deterministic
trajectory optimization problem is solved using a black-box simulation model of
the dynamical system using a standard nonlinear programming (NLP) solver. Then
a Linear Quadratic Regulator (LQR) controller is designed for the nominal
trajectory-dependent linearized system which is learned using input-output
experimental data. Computational examples are used to illustrate the
performance of the proposed approach with three benchmark problems.
