Information Extensive Form Game

Information extensive-form games (IEFGs) model sequential decision-making under imperfect information, aiming to find optimal strategies or equilibrium solutions for multiple interacting agents. Current research focuses on developing efficient algorithms, such as policy gradient methods and counterfactual regret minimization (CFR), to solve these games, particularly addressing challenges posed by large action spaces and limited feedback (bandit settings). These advancements improve the efficiency of finding approximate Nash equilibria and correlated equilibria, with implications for game theory, multi-agent reinforcement learning, and applications in areas like poker and negotiation.