Extensive Form Game

Extensive-form games model sequential decision-making scenarios where players take turns, possessing varying levels of information about past actions. Current research focuses on developing efficient algorithms, such as those based on mirror descent and counterfactual regret minimization, to find equilibrium solutions, particularly in large-scale games with imperfect information. These advancements are driven by the need for computationally tractable methods and improved convergence rates, impacting fields like AI, economics, and cybersecurity where strategic interactions are crucial. The development of efficient libraries and improved understanding of the computational complexity of finding equilibria are also key themes.