# An efficient numerical algorithm for solving data driven feedback control problems

arXiv: https://arxiv.org/abs/2006.03047 · HTML: https://mlsanity.com/paper/2006.03047/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations](https://mlsanity.com/paper/2605.17107/) (2026)
- [Federated Learning on Stochastic Neural Networks](https://mlsanity.com/paper/2506.08169/) (2025)
- [SDE Guided Monte Carlo Reinforcement Learning: A Stochastic Maximum Principle Approach for Robust Decision Making in Noisy Environments](https://mlsanity.com/paper/2607.22541/) (2026)

## Abstract (arXiv)

The goal of this paper is to solve a class of stochastic optimal control problems numerically, in which the state process is governed by an Ito type stochastic differential equation with control process entering both in the drift and the diffusion, and is observed partially. The optimal control of feedback form is determined based on the available observational data. We call this type of control problems the data driven feedback control. The computational framework that we introduce to solve such type of problems aims to find the best estimate for the optimal control as a conditional expectation given the observational information. To make our method feasible in providing timely feedback to the controlled system from data, we develop an efficient stochastic optimization algorithm to implement our computational framework.