# Episodic Linear Quadratic Regulators with Low-rank Transitions

arXiv: https://arxiv.org/abs/2011.01568 · HTML: https://mlsanity.com/paper/2011.01568/

## Key facts

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

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

- [Sparsity in Partially Controllable Linear Systems](https://mlsanity.com/paper/2110.06150/) (2021)
- [Adaptive Control of Differentially Private Linear Quadratic Systems](https://mlsanity.com/paper/2108.11563/) (2021)
- [Learning to Control under Time-Varying Environment](https://mlsanity.com/paper/2206.02507/) (2022)

## Abstract (arXiv)

Linear Quadratic Regulators (LQR) achieve enormous successful real-world applications. Very recently, people have been focusing on efficient learning algorithms for LQRs when their dynamics are unknown. Existing results effectively learn to control the unknown system using number of episodes depending polynomially on the system parameters, including the ambient dimension of the states. These traditional approaches, however, become inefficient in common scenarios, e.g., when the states are high-resolution images. In this paper, we propose an algorithm that utilizes the intrinsic system low-rank structure for efficient learning. For problems of rank-m, our algorithm achieves a K-episode regret bound of order widetildeO(m3/2 K1/2). Consequently, the sample complexity of our algorithm only depends on the rank, m, rather than the ambient dimension, d, which can be orders-of-magnitude larger.