# Adaptive Control of Unknown Linear Switched Systems via Policy Gradient Methods

arXiv: https://arxiv.org/abs/2601.03016 · HTML: https://mlsanity.com/paper/2601.03016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 6 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, 19 of 19)

- [Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator](https://mlsanity.com/paper/1801.05039/) (2018)
- [Naive Exploration is Optimal for Online LQR](https://mlsanity.com/paper/2001.09576/) (2020)
- [Learning Linear-Quadratic Regulators Efficiently with only √T Regret](https://mlsanity.com/paper/1902.06223/) (2019)
- [Online Linear Quadratic Control](https://mlsanity.com/paper/1806.07104/) (2018)
- [Statistical Learning Theory for Control: A Finite Sample Perspective](https://mlsanity.com/paper/2209.05423/) (2022)
- [Formulas for Data-driven Control: Stabilization, Optimality and Robustness](https://mlsanity.com/paper/1903.06842/) (2019)
- [Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem](https://mlsanity.com/paper/1912.11899/) (2019)
- [Data-Enabled Predictive Control: In the Shallows of the DeePC](https://mlsanity.com/paper/1811.05890/) (2018)
- [Data-Driven Model Predictive Control with Stability and Robustness Guarantees](https://mlsanity.com/paper/1906.04679/) (2019)
- [Data informativity: a new perspective on data-driven analysis and control](https://mlsanity.com/paper/1908.00468/) (2019)
- [Data-Enabled Policy Optimization for Direct Adaptive Learning of the LQR](https://mlsanity.com/paper/2401.14871/) (2024)
- [On the Certainty-Equivalence Approach to Direct Data-Driven LQR Design](https://mlsanity.com/paper/2109.06643/) (2021)
- [Policy Gradient Adaptive Control for the LQR: Indirect and Direct Approaches](https://mlsanity.com/paper/2505.03706/) (2025)
- [Exact Asymptotics for Linear Quadratic Adaptive Control](https://mlsanity.com/paper/2011.01364/) (2020)
- [Online learning of data-driven controllers for unknown switched linear systems](https://mlsanity.com/paper/2105.11523/) (2021)
- [Direct Adaptive Control of Grid-Connected Power Converters via Output-Feedback Data-Enabled Policy Optimization](https://mlsanity.com/paper/2411.03909/) (2024)
- [Data-driven stabilization of switched and constrained linear systems](https://mlsanity.com/paper/2208.11392/) (2022)
- [An Online Adaptation Strategy for Direct Data-driven Control](https://mlsanity.com/paper/2304.03386/) (2023)
- [Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems](https://mlsanity.com/paper/2401.15278/) (2024)

## Abstract (arXiv)

We consider the policy gradient adaptive control (PGAC) framework, which adaptively updates a control policy in real time, by performing data-based gradient descent steps on the linear quadratic regulator cost. This method has empirically shown to react to changing circumstances, such as model parameters, efficiently. To formalize this observation, we design a PGAC method which stabilizes linear switched systems, where both model parameters and switching time are unknown. We use sliding window data for the policy gradient estimate and show that under a dwell time condition and small dynamics variation, the policy can track the switching dynamics and ensure closed-loop stability. We perform simulations to validate our theoretical results.