论文标题
数据驱动的策略迭代算法，用于连续时间随机线性季度最佳控制问题
Data-driven policy iteration algorithm for continuous-time stochastic linear-quadratic optimal control problems
论文作者
论文摘要
本文研究了无限 - 摩尼子上的连续时间随机线性季节（SLQ）最佳控制问题。提出了一个数据驱动的策略迭代算法来解决SLQ问题。在不知道三个系统系数矩阵的情况下，该算法使用收集的数据迭代近似于相应的随机代数riccati方程（SARE）的解决方案。提供了一个模拟示例来说明算法的有效性和适用性。
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.
