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arXiv · 2209.14490

Data-driven policy iteration algorithm for continuous-time stochastic linear-quadratic optimal control problems

Heng Zhang·Na Li
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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.

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Heng Zhang, Na Li. 2022-09-29. Data-driven policy iteration algorithm for continuous-time stochastic linear-quadratic optimal control problems. https://arxiv.org/abs/2209.14490

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