Card Game

Research on card games within artificial intelligence focuses on developing AI agents capable of mastering complex games with imperfect information and vast state spaces, often employing reinforcement learning techniques like Deep Monte Carlo and variations of fictitious play. Current efforts concentrate on improving model architectures, such as transformers and neural networks, to handle the challenges posed by large action spaces and multi-stage gameplay, as seen in games like Doudizhu and Hearthstone. This research contributes to advancements in AI algorithms for imperfect information games and provides valuable insights into decision-making under uncertainty, with applications extending beyond game playing to other fields requiring strategic planning.