# Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective

arXiv: https://arxiv.org/abs/2203.12044 · HTML: https://mlsanity.com/paper/2203.12044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2022.

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

- [Stability of Q-Learning Through Design and Optimism](https://mlsanity.com/paper/2307.02632/) (2023)

## Abstract (arXiv)

In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming. First, we introduce a unified framework for the fixed point characterization of the value function, Q-function and relaxed Bellman operators. Then, in a model-free setting, we show how to synthesize and estimate Bellman inequalities from a small but sufficiently rich dataset. To guarantee exploration richness, we complete the extension of Willem's fundamental lemma to affine systems.