Contemporary work on learning in continuous games has commonly overlooked the
hierarchical decision-making structure present in machine learning problems formulated as …

We study gradient descent-ascent learning dynamics with timescale separation ($\tau $-
GDA) in unconstrained continuous action zero-sum games where the minimizing player …

We study the role that a finite timescale separation parameter τ has on gradient descent-
ascent in non-convex, non-concave zero-sum games where the learning rate of player 1 is …

We show by counterexample that policy-gradient algorithms have no guarantees of even
local convergence to Nash equilibria in continuous action and state space multi-agent …

Many recent AI architectures are inspired by zero-sum games, however, the behavior of their
dynamics is still not well understood. Inspired by this, we study standard gradient descent …

We study the role that a finite timescale separation parameter $\tau $ has on gradient
descent-ascent in two-player non-convex, non-concave zero-sum games where the learning …

Game-theoretic formulations in machine learning have recently risen in prominence,
whereby entire modeling paradigms are best captured as zero-sum games. Despite their …

Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local
optimizer, algorithms for nonconvex-nonconcave minimax optimization can have …

Finding equilibria points in continuous minimax games has become a key problem within
machine learning, in part due to its connection to the training of generative adversarial …

Worst-case analysis (WCA) has been the dominant tool for understanding the performance
of the lion share of algorithmic arsenal of theoretical computer science. While WCA has …