Counterfactual Regret Minimization

Counterfactual Regret Minimization (CFR) is a family of algorithms designed to solve large-scale imperfect-information games by iteratively minimizing regret, a measure of how poorly an agent performed compared to alternative actions. Current research focuses on improving CFR's efficiency through techniques like GPU acceleration, optimistic online mirror descent variants for faster convergence, and integrating it with other methods such as Boltzmann Q-learning or large language models for enhanced performance in complex games like poker and Mahjong. These advancements are significant because they enable the solution of increasingly complex games, pushing the boundaries of artificial intelligence and offering potential applications in areas like negotiation, resource management, and security.