Zero Sum

Zero-sum games model competitive scenarios where one player's gain is exactly balanced by another's loss. Current research focuses on developing efficient algorithms to solve these games, particularly within complex frameworks like partially observable stochastic games and multi-player Markov games, often employing techniques like value iteration, policy iteration, and linear programming, sometimes enhanced by representation learning or subgame resolving. These advancements aim to improve the scalability and applicability of zero-sum game solutions in diverse fields, ranging from artificial intelligence and game theory to robotics and economics. The ultimate goal is to find optimal or near-optimal strategies in increasingly complex and realistic settings.