We introduce families of information theoretic divergences between probability measure which are constructed as Moreau-Yosida regularization of f-divergence (e.g. KL-divergences) with a Wasserstein metrics. We discuss the fundamental properties of those divergence as well as their applications to generative modeling and their analysis using mean-field games tools.