Min Max Game

Min-max games, also known as saddle point problems, involve finding optimal strategies for competing players with opposing objectives. Current research focuses on developing and analyzing algorithms for solving these games, particularly in high-dimensional settings relevant to machine learning, including gradient-based methods and variations of Q-learning. These games are crucial for understanding and improving robustness in various applications, such as generative adversarial networks, reinforcement learning, and Bayesian neural networks, by providing a framework for modeling adversarial interactions and uncertainty. The development of efficient and provably convergent algorithms for min-max games is driving progress in these fields.