Non Cooperative Game

Non-cooperative game theory studies strategic interactions where agents independently pursue their self-interest, aiming to find equilibrium solutions like Nash equilibria. Current research focuses on developing efficient algorithms for finding equilibria in complex settings, including those with incomplete information, many agents (using mean-field game approximations), and coupled constraints (generalized Nash equilibria), often employing techniques like reinforcement learning, gradient descent, and information compression to manage computational complexity. These advancements have significant implications for diverse fields, enabling better modeling and analysis of multi-agent systems in areas such as robotics, economics, and resource management.