Differentiable Game

Differentiable games leverage the power of automatic differentiation to analyze and solve game-theoretic problems, particularly in multi-agent systems where agents' actions interdependently affect outcomes. Current research focuses on developing and analyzing efficient algorithms, such as gradient methods and their momentum-based variants, for finding equilibria in various game settings, including zero-sum and Stackelberg games, often employing neural networks for function approximation and optimization. This approach has significant implications for diverse fields, enabling improved design of automated market makers, more robust multi-agent reinforcement learning, and enhanced solutions for problems in robotics and auction theory.