Equilibrium Policy

Equilibrium policy research focuses on finding optimal strategies for agents in multi-agent systems, aiming to achieve a stable state where no agent can unilaterally improve its outcome. Current research emphasizes developing efficient algorithms, such as Policy-Space Response Oracles (PSRO) and variations incorporating self-adaptive hyperparameter optimization and smoothing techniques, to solve for equilibrium policies in various game settings, including zero-sum and general-sum games with both discrete and continuous state spaces. These methods are applied to diverse problems, from target guarding and inventory control to swarm robotics and autonomous driving, leveraging models like Hamilton-Jacobi-Isaacs equations and contextual bandits. The resulting advancements offer improved performance and robustness in complex multi-agent scenarios, impacting fields ranging from control theory to artificial intelligence.