Poker is in the family of imperfect information games unlike other games such as chess, connect four, etc which are perfect information game instead. While many perfect information games have been solved, no non-trivial imperfect information game has been solved to date. This makes poker a great test bed for Artificial Intelligence research. In this paper we firstly compare Game theory optimal poker to Exploitative poker. Secondly, we discuss the intricacies of abstraction techniques, betting models, and specific strategies employed by successful poker bots like Tartanian[1] and Pluribus[6]. Thirdly, we also explore 2-player vs multi-player games and the limitations that come when playing with more players. Finally, this paper discusses the role of machine learning and theoretical approaches in developing winning strategies and suggests future directions for this rapidly evolving field.

扑克是一种不完全信息游戏，本文比较了理论最优扑克与剥削式扑克，并讨论了抽象技术、投注模型和成功扑克机器人（如Tartanian和Pluribus）所使用的具体策略；同时探讨了两人对战与多人对战游戏以及在与更多玩家对战时出现的限制；最后，本文讨论了机器学习和理论方法在开发获胜策略中的作用，并对这个快速发展领域的未来方向提出了建议。

关于博弈论最优扑克的调查