# On Robust Control of Partially Observed Uncertain Systems with Additive Costs

arXiv: https://arxiv.org/abs/2209.13787 · HTML: https://mlsanity.com/paper/2209.13787/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2022.

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

- [Approximate Information States for Worst-Case Control and Learning in Uncertain Systems](https://mlsanity.com/paper/2301.05089/) (2023)
- [Worst-Case Control and Learning Using Partial Observations Over an Infinite Time-Horizon](https://mlsanity.com/paper/2303.16321/) (2023)

## Abstract (arXiv)

In this paper, we consider the problem of optimizing the worst-case behavior of a partially observed system. All uncontrolled disturbances are modeled as finite-valued uncertain variables. Using the theory of cost distributions, we present a dynamic programming (DP) approach to compute a control strategy that minimizes the maximum possible total cost over a given time horizon. To improve the computational efficiency of the optimal DP, we introduce a general definition for information states and show that many information states constructed in previous research efforts are special cases of ours. Additionally, we define approximate information states and an approximate DP that can further improve computational tractability by conceding a bounded performance loss. We illustrate the utility of these results using a numerical example.