  We prove that differential Nash equilibria are generic amongst local Nash
equilibria in continuous zero-sum games. That is, there exists an open-dense
subset of zero-sum games for which local Nash equilibria are non-degenerate
differential Nash equilibria. The result extends previous results to the
zero-sum setting, where we obtain even stronger results; in particular, we show
that local Nash equilibria are generically hyperbolic critical points. We
further show that differential Nash equilibria of zero-sum games are
structurally stable. The purpose for presenting these extensions is the recent
renewed interest in zero-sum games within machine learning and optimization.
Adversarial learning and generative adversarial network approaches are touted
to be more robust than the alternative. Zero-sum games are at the heart of such
approaches. Many works proceed under the assumption of hyperbolicity of
critical points. Our results justify this assumption by showing `almost all'
zero-sum games admit local Nash equilibria that are hyperbolic.
