  This paper studies a continuous-time stochastic linear-quadratic (SLQ)
optimal control problem on infinite-horizon. A data-driven policy iteration
algorithm is proposed to solve the SLQ problem. Without knowing three system
coefficient matrices, this algorithm uses the collected data to iteratively
approximate a solution of the corresponding stochastic algebraic Riccati
equation (SARE). A simulation example is provided to illustrate the
effectiveness and applicability of the algorithm.
