# Model-Free Output Feedback Stabilization via Policy Gradient Methods

arXiv: https://arxiv.org/abs/2601.19284 · HTML: https://mlsanity.com/paper/2601.19284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 24 of 24)

- [Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Condition](https://mlsanity.com/paper/1608.04636/) (2016)
- [Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator](https://mlsanity.com/paper/1801.05039/) (2018)
- [On the Sample Complexity of the Linear Quadratic Regulator](https://mlsanity.com/paper/1710.01688/) (2017)
- [A Tour of Reinforcement Learning: The View from Continuous Control](https://mlsanity.com/paper/1806.09460/) (2018)
- [Learning Linear-Quadratic Regulators Efficiently with only √T Regret](https://mlsanity.com/paper/1902.06223/) (2019)
- [Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems](https://mlsanity.com/paper/1812.08305/) (2018)
- [Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem](https://mlsanity.com/paper/1912.11899/) (2019)
- [Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control](https://mlsanity.com/paper/2102.04393/) (2021)
- [Optimizing Static Linear Feedback: Gradient Method](https://mlsanity.com/paper/2004.09875/) (2020)
- [Stabilizing Dynamical Systems via Policy Gradient Methods](https://mlsanity.com/paper/2110.06418/) (2021)
- [Global Convergence of Policy Gradient Primal-dual Methods for Risk-constrained LQRs](https://mlsanity.com/paper/2104.04901/) (2021)
- [Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret](https://mlsanity.com/paper/2102.12608/) (2021)
- [On the Optimization Landscape of Dynamic Output Feedback Linear Quadratic Control](https://mlsanity.com/paper/2201.09598/) (2022)
- [Optimization Landscape of Gradient Descent for Discrete-time Static Output Feedback](https://mlsanity.com/paper/2109.13132/) (2021)
- [On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory](https://mlsanity.com/paper/2202.07187/) (2022)
- [Convergence and Sample Complexity of Policy Gradient Methods for Stabilizing Linear Systems](https://mlsanity.com/paper/2205.14335/) (2022)
- [On Topological Properties of the Set of Stabilizing Feedback Gains](https://mlsanity.com/paper/1904.08451/) (2019)
- [Globally Convergent Policy Gradient Methods for Linear Quadratic Control of Partially Observed Systems](https://mlsanity.com/paper/2211.04051/) (2022)
- [Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback](https://mlsanity.com/paper/2310.19022/) (2023)
- [Learning Stabilizing Policies via an Unstable Subspace Representation](https://mlsanity.com/paper/2505.01348/) (2025)
- [On Regularizability and its Application to Online Control of Unstable LTI Systems](https://mlsanity.com/paper/2006.00125/) (2020)
- [Model-Free Learning for the Linear Quadratic Regulator over Rate-Limited Channels](https://mlsanity.com/paper/2401.01258/) (2024)
- [Stabilizing Linear Systems under Partial Observability: Sample Complexity and Fundamental Limits](https://mlsanity.com/paper/2503.16756/) (2025)
- [Policy Gradient Method for LQG Control via Input-Output-History Representation: Convergence to O(ε)-Stationary Points](https://mlsanity.com/paper/2510.19141/) (2025)

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

- [Online Learning of Kalman Filtering: From Output to State Estimation](https://mlsanity.com/paper/2603.27159/) (2026)

## Abstract (arXiv)

Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. The problem becomes challenging when the system model is unknown. Among the Reinforcement Learning (RL) algorithms that have been successfully applied to solve problems pertaining to unknown linear dynamical systems, the policy gradient (PG) method stands out due to its ease of implementation and can solve the problem in a model-free manner. However, most of the existing works on PG methods for unknown linear dynamical systems assume full-state feedback. In this paper, we take a step towards model-free learning for partially observed linear dynamical systems with output feedback and focus on the fundamental stabilization problem of the system. We propose an algorithmic framework that stretches the boundary of PG methods to the problem without global convergence guarantees. We show that by leveraging zeroth-order PG update based on system trajectories and its convergence to stationary points, the proposed algorithms return a stabilizing output feedback policy for discrete-time linear dynamical systems. We also explicitly characterize the sample complexity of our algorithm and verify the effectiveness of the algorithm using numerical examples.