  This paper presents a machine learning approach for tuning the parameters of
a family of stabilizing controllers for orbital tracking. An augmented random
search algorithm is deployed, which aims at minimizing a cost function
combining convergence time and fuel consumption. The main feature of the
proposed learning strategy is that closed-loop stability is always guaranteed
during the exploration of the parameter space, {a property that allows one to
streamline the training process by restricting the search domain to
well-behaved control policies.} The proposed approach is tested on two case
studies: an orbital transfer and a rendezvous and docking mission. It is shown
that in both cases the learned control parameters lead to a significant
improvement of the considered performance measure.
