Stochastic Game

Stochastic games model strategic interactions among multiple agents in uncertain environments, aiming to find optimal strategies or equilibria that maximize individual or collective rewards. Current research emphasizes developing efficient algorithms for solving these games, particularly focusing on reinforcement learning techniques like Q-learning and policy iteration, often adapted for specific game structures (e.g., zero-sum, cooperative) and incorporating neural networks for complex state spaces. This field is significant for its applications in diverse areas such as robotics, economics, and security, providing a theoretical framework for analyzing and designing optimal decision-making in multi-agent systems with uncertainty.