Handling Incomplete Information and Bluffing Researchers are exploring various machine learning techniques, such as reinforcement learning, to enable AI algorithms to learn from their experiences and adjust their strategies accordingly, as seen in the poker AI algorithm used in online AI poker platforms. This results in a constantly shifting game dynamic, requiring AI systems like Holdem AI to adapt in real-time. One key difference is the variable stack sizes and the ability to replenish chips at any time. Adapting to Cash Game DynamicsĬash games present a unique set of challenges compared to tournament poker. This article will discuss the ongoing efforts of developmental scientists in addressing these challenges using advanced statistical and machine learning techniques, with a focus on AI systems like Libratus AI, poker game AI, and Pluribus poker AI. Despite significant progress in recent years, including the development of Facebook’s poker AI and AI poker apps, several challenges persist, particularly in the domain of cash games. The game’s inherent complexity and the necessity to make decisions based on incomplete information make it an ideal testing ground for AI systems such as AI Libratus and Pluribus AI. Poker has long been a captivating subject for researchers in artificial intelligence (AI), machine learning, and statistics.