  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.
