Data-driven policy iteration algorithm for continuous-time stochastic linear-quadratic optimal control problems
Optimization and Control
2022-09-30 v1
Abstract
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.
Cite
@article{arxiv.2209.14490,
title = {Data-driven policy iteration algorithm for continuous-time stochastic linear-quadratic optimal control problems},
author = {Heng Zhang and Na Li},
journal= {arXiv preprint arXiv:2209.14490},
year = {2022}
}