# Adaptive dynamic programming-based algorithm for infinite-horizon linear quadratic stochastic optimal control problems

arXiv: https://arxiv.org/abs/2210.04486 · HTML: https://mlsanity.com/paper/2210.04486/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2022.

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning Method for Zero-Sum Linear-Quadratic Stochastic Differential Games in Infinite Horizons](https://mlsanity.com/paper/2602.08075/) (2026)

## Abstract (arXiv)

This paper investigates an infinite-horizon linear quadratic stochastic (LQS) optimal control problem for a class of continuous-time stochastic systems. By employing the technique of adaptive dynamic programming (ADP), we propose a novel model-free policy iteration (PI) algorithm. Without needing all information of the system coefficient matrices, the proposed PI algorithm iterates by using the data of the input and system state collected on a fixed time interval. Finally, a numerical example is presented to demonstrate the feasibility of the obtained algorithm.