Large Scale Game

Large-scale game research focuses on developing efficient algorithms and models to analyze and solve games with numerous interacting agents, aiming to find equilibrium solutions or optimal strategies. Current research emphasizes mean-field games (MFGs) and policy space response oracles (PSROs) to approximate equilibria in large populations, often incorporating techniques like regret matching and mirror descent to improve convergence and stability. These advancements are significant for tackling computationally challenging problems in diverse fields, including multi-agent reinforcement learning, air traffic control, and human-AI interaction, where traditional game-theoretic methods are intractable.