Two Player Zero Sum

Two-player zero-sum games model competitive scenarios where one player's gain is the other's loss, focusing on finding optimal strategies (Nash equilibria). Current research emphasizes efficient algorithms like Policy Space Response Oracles (PSRO) and their variants, including self-adaptive and anytime versions, to solve large-scale games, often incorporating reinforcement learning and addressing challenges like last-iterate convergence and handling imperfect information. These advancements have implications for diverse fields, improving the efficiency of game solving in areas such as AI, robotics, and automated negotiation, while also furthering our theoretical understanding of equilibrium computation in complex systems.