Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior

arXiv:2310.14098v2 Announce Type: replace
Abstract: 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. Perhaps of independent interest, we formulate and analyze the stability of …

abstract advances advantages arxiv behavior control cs.ai cs.lg cs.sy design domain eess.sy feedback framework free math.oc modular optimization reinforcement reinforcement learning search stability systems type