Global Convergence of Policy Gradient Algorithms for Indefinite Least Squares Stationary Optimal Control
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1.
3 papers.
- 2021Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexitycited by 15
- 2022Towards a Theoretical Foundation of Policy Optimization for Learning Control Policiescited by 6
- 2023Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergencecited by 3
- 2023Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence8 Sep 2023
- 2022Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies10 Oct 2022
- 2021Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity4 Jan 2021