General Sum Game

General-sum games model multi-agent interactions where agents' payoffs are interdependent but not necessarily diametrically opposed, encompassing cooperation and competition. Current research focuses on developing efficient algorithms, such as those based on optimistic gradient methods and opponent shaping, to find Nash equilibria or correlated equilibria in these complex scenarios, often addressing challenges posed by high-dimensionality and imperfect information. These advancements have implications for multi-agent reinforcement learning, enabling more sophisticated and robust AI agents capable of navigating real-world situations with mixed incentives, and informing the design of more effective mechanisms in areas like market economics.