# An Online Algorithm for Solving Feedback Optimal Control Problems with Partial Observations

arXiv: https://arxiv.org/abs/2404.05734 · HTML: https://mlsanity.com/paper/2404.05734/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting](https://mlsanity.com/paper/2605.29072/) (2026)
- [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)

This paper presents a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assimilation techniques and optimal control solvers to manage partial observation of the state process, a common occurrence in practical scenarios. Traditional stochastic optimal control methods assume full state observation, which is often not feasible in real-world applications. Our approach underscores the significance of utilizing observational data to inform control policy design. Specifically, we introduce a kernel learning backward stochastic differential equation (SDE) filter to enhance data assimilation efficiency and propose a sample-wise stochastic optimization method within the stochastic maximum principle framework. Numerical experiments validate the efficacy and accuracy of our algorithm, showcasing its high efficiency in solving feedback optimal control problems with partial observation.