Two Player

Two-player game theory investigates strategic interactions between two agents, aiming to understand optimal decision-making and equilibrium outcomes. Current research focuses on developing algorithms for finding Nash equilibria in various game settings, including zero-sum and general-sum games, often employing techniques like reinforcement learning, mirror descent, and Thompson sampling, and adapting these to handle imperfect information and large state spaces. This field is crucial for advancing artificial intelligence, particularly in areas like large language model alignment, multi-agent systems, and the development of robust and secure AI agents.