Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

We consider nonconvex-concave minimax problems, $\min_ {\mathbf {x}}\max_ {\mathbf
{y}\in\mathcal {Y}} f (\mathbf {x},\mathbf {y}) $, where $ f $ is nonconvex in $\mathbf {x} $ but …

We obtain global, non-asymptotic convergence guarantees for independent learning
algorithms in competitive reinforcement learning settings with two agents (ie, zero-sum …

Minimax optimization has found extensive applications in modern machine learning, in
settings such as generative adversarial networks (GANs), adversarial training and multi …

Recent applications that arise in machine learning have surged significant interest in solving
min-max saddle point games. This problem has been extensively studied in the convex …

An important problem that arises in reinforcement learning and Monte Carlo methods is
estimating quantities defined by the stationary distribution of a Markov chain. In many real …

The use of min-max optimization in the adversarial training of deep neural network
classifiers, and the training of generative adversarial networks has motivated the study of …

Modern minimax problems, such as generative adversarial network and adversarial training,
are often under a nonconvex-nonconcave setting, and developing an efficient method for …

The extragradient (EG), introduced by GM Korpelevich in 1976, is a well-known method to
approximate solutions of saddle-point problems and their extensions such as variational …

Nonconvex minimax problems appear frequently in emerging machine learning
applications, such as generative adversarial networks and adversarial learning. Simple …