From Scalar Games to Unconstrained Network Zero-Sum Games: Transfer Principles for Gradient-Based Learning

We show that, for a broad class of gradient-based learning rules, scalar zero-sum games form a complete test family for convergence in unconstrained network zero-sum games. Specifically, suppose a learning rule achieves last-iterate or time-average convergence on every scalar game of magnitude $a$ w