Approximate Nash Equilibrium

Approximate Nash equilibrium (ANE) research focuses on efficiently finding near-optimal solutions in multi-agent games where computing exact Nash equilibria is computationally intractable. Current research emphasizes developing decentralized learning algorithms, often employing techniques like mean-field approximations, actor-critic methods, and zeroth-order optimization for continuous strategy spaces, sometimes incorporating neural networks for function approximation. These advancements are significant for tackling large-scale games in diverse fields such as reinforcement learning, auction design, and dynamic pricing, offering practical solutions to complex strategic interactions.