Researchers introduce Regret-Weighted Payoff Sampling (RWPS), a new algorithm for efficiently computing Nash equilibria in cybersecurity games. RWPS estimates payoffs by simulating only the cells an equilibrium is sensitive to and using a surrogate model for the rest. This approach provides tighter bounds and better performance compared to existing methods, particularly in growing-pool PSRO. This development is relevant to people building and operating AI agents as it can improve the security and performance of AI-powered systems in cybersecurity applications.