# Policy Gradient Methods for Designing Dynamic Output Feedback Controllers

arXiv: https://arxiv.org/abs/2210.09735 · HTML: https://mlsanity.com/paper/2210.09735/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Policy Gradient Method for LQG Control via Input-Output-History Representation: Convergence to O(ε)-Stationary Points](https://mlsanity.com/paper/2510.19141/) (2025)
- [On the Optimization Landscape of Observer-based Dynamic Linear Quadratic Control](https://mlsanity.com/paper/2604.10635/) (2026)

## Abstract (arXiv)

This paper proposes model-based and model-free policy gradient methods (PGMs) for designing dynamic output feedback controllers for discrete-time partially observable systems. To fulfill this objective, we first show that any dynamic output feedback controller design is equivalent to a state-feedback controller design for a newly introduced system whose internal state is a finite-length input-output history (IOH). Next, based on this equivalency, we propose a model-based PGM and show its global linear convergence by proving that the Polyak-Lojasiewicz inequality holds for a reachability-based lossless projection of the IOH dynamics. Moreover, we propose two model-free implementations of the PGM: the multi- and single-episodic PGM. The former is a Monte Carlo approximation of the model-based PGM, whereas the latter is a simplified version of the former for ease of use in real systems. A sample complexity analysis of both methods is also presented. Finally, the effectiveness of the model-based/model-free PGMs is investigated through a numerical simulation.