Zero Sum Matrix Game

Zero-sum matrix games model strategic interactions where one player's gain is exactly balanced by another's loss. Current research focuses on developing efficient algorithms, such as variations of multiplicative weight updates and best-response dynamics, to find Nash equilibria (optimal strategies) in these games, particularly addressing challenges posed by stochasticity, uncertainty, and multiple players. These advancements improve our understanding of learning dynamics in competitive settings and have implications for areas like reinforcement learning, game theory, and the design of robust AI agents. The development of instance-dependent bounds and the exploration of polymatrix games are also key themes, aiming to provide more precise and efficient solutions.