An imperfect-information game is a type of game with asymmetric information. It is more common in life than perfect-information game. Artificial intelligence (AI) in imperfect-information games, such like poker, has made considerable progress and success in recent years. The great success of superhuman poker AI, such as Libratus and Deepstack, attracts researchers to pay attention to poker research. However, the lack of open-source code limits the development of Texas hold'em AI to some extent. This article introduces DecisionHoldem, a high-level AI for heads-up no-limit Texas hold'em with safe depth-limited subgame solving by considering possible ranges of opponent's private hands to reduce the exploitability of the strategy. Experimental results show that DecisionHoldem defeats the strongest openly available agent in heads-up no-limit Texas hold'em poker, namely Slumbot, and a high-level reproduction of Deepstack, viz, Openstack, by more than 730 mbb/h (one-thousandth big blind per round) and 700 mbb/h. Moreover, we release the source codes and tools of DecisionHoldem to promote AI development in imperfect-information games.

本文介绍了 DecisionHoldem，一种高级 AI，可通过安全深度限制子游戏解决来降低对手的可能手牌范围，以减少策略的可利用性，实验结果表明，DecisionHoldem 战胜了 heads-up no-limit Texas hold'em 扑克中最强的公开可用代理 Slumbot 和 Deepstack 的高水平繁殖，即 OpenStack，超过了 730 mbb/h 和 700mbb/h。此外，我们公开了 DecisionHoldem 的源代码和工具，以促进不完全信息游戏中的人工智能发展。

DecisionHoldem: 不完全信息游戏的安全深度限制求解与多样对手