Nash Equilibrium

Nash equilibrium, a cornerstone of game theory, describes a stable state in a game where no player can improve their outcome by unilaterally changing their strategy. Current research focuses on developing efficient algorithms, such as those based on reinforcement learning, online learning, and Gaussian processes, to compute Nash equilibria in increasingly complex scenarios, including multi-agent systems, Markov games, and games with incomplete information. These advancements are crucial for addressing challenges in diverse fields like robotics, resource allocation, and cybersecurity, where understanding strategic interactions between agents is paramount for designing effective and robust systems.