We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; this enables an alternative realization of the Youla-Kucera parameterization based entirely on input-output exploration data. Using a neural network to express a parameterized set of nonlinear stable operators enables seamless integration with standard deep learning libraries. We demonstrate the approach on a realistic simulation of a two-tank system.
@article{arxiv.2304.03422,
title = {A modular framework for stabilizing deep reinforcement learning control},
author = {Nathan P. Lawrence and Philip D. Loewen and Shuyuan Wang and Michael G. Forbes and R. Bhushan Gopaluni},
journal= {arXiv preprint arXiv:2304.03422},
year = {2024}
}