# Global Convergence of Policy Gradient Algorithms for Indefinite Least Squares Stationary Optimal Control

arXiv: https://arxiv.org/abs/2002.05023 · HTML: https://mlsanity.com/paper/2002.05023/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

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

- [Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity](https://mlsanity.com/paper/2101.01041/) (2021)
- [Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies](https://mlsanity.com/paper/2210.04810/) (2022)
- [Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence](https://mlsanity.com/paper/2309.04272/) (2023)

## Abstract (arXiv)

We consider policy gradient algorithms for the indefinite least squares stationary optimal control, e.g., linear-quadratic-regulator (LQR) with indefinite state and input penalization matrices. Such a setup has important applications in control design with conflicting objectives, such as linear quadratic dynamic games. We show the global convergence of gradient, natural gradient and quasi-Newton policies for this class of indefinite least squares problems.