  This paper presents a parameterization of nonlinear controllers for uncertain
systems building on a recently developed neural network architecture, called
the recurrent equilibrium network (REN), and a nonlinear version of the Youla
parameterization. The proposed framework has "built-in" guarantees of
stability, i.e., all policies in the search space result in a contracting
(globally exponentially stable) closed-loop system. Thus, it requires very mild
assumptions on the choice of cost function and the stability property can be
generalized to unseen data. Another useful feature of this approach is that
policies are parameterized directly without any constraints, which simplifies
learning by a broad range of policy-learning methods based on unconstrained
optimization (e.g. stochastic gradient descent). We illustrate the proposed
approach with a variety of simulation examples.
