Breakthrough applications of artificial intelligence in Texas Hold'em poker

The breakthrough progress of artificial intelligence in the field of incomplete information games has promoted the deep integration of game theory and machine learning technology with Texas Hold'em as a typical scenario. From a multi-dimensional technical perspective, this article systematically reviews the research progress of artificial intelligence in the field of Texas Hold'em, covering theoretical frameworks, core methods and technological breakthroughs, and looks forward to future development directions. The research focuses on three core technical directions: counterfactual regret minimization (CFR) and its improved algorithms, deep reinforcement learning (DRL) and swarm intelligence optimization. At the same time, this article points out the current challenges such as high computational complexity and insufficient dynamic adaptability, and proposes to optimize computing power constraints through lightweight models, meta-learning and quantum computing, and develop collaborative game theory and cross-domain migration frameworks. Progress in this field has also promoted the migration and application of core algorithms to more complex scenarios, providing a general paradigm for intelligent reasoning in uncertain environments.