# Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator

arXiv: https://arxiv.org/abs/2011.07011 · HTML: https://mlsanity.com/paper/2011.07011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

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

- [A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks](https://mlsanity.com/paper/2102.00573/) (2021)

## Abstract (arXiv)

This paper discusses learning a structured feedback control to obtain sufficient robustness to exogenous inputs for linear dynamic systems with unknown state matrix. The structural constraint on the controller is necessary for many cyber-physical systems, and our approach presents a design for any generic structure, paving the way for distributed learning control. The ideas from reinforcement learning (RL) in conjunction with control-theoretic sufficient stability and performance guarantees are used to develop the methodology. First, a model-based framework is formulated using dynamic programming to embed the structural constraint in the linear quadratic regulator (LQR) setting along with sufficient robustness conditions. Thereafter, we translate these conditions to a data-driven learning-based framework - robust structured reinforcement learning (RSRL) that enjoys the control-theoretic guarantees on stability and convergence. We validate our theoretical results with a simulation on a multi-agent network with 6 agents.