论文标题
OpenHoldem：大规模不完美信息游戏的基准
OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
论文作者
论文摘要
在一些机构的不懈努力中，最近在不受限制的德克萨斯州Holdem（NLTH）设计超人AIS方面取得了重大进展，这是大规模不完美信息游戏研究的主要测试床。但是，对于新研究人员来说，研究此问题仍然具有挑战性，因为没有标准基准可以与现有方法进行比较，这严重阻碍了该研究领域的进一步发展。在这项工作中，我们介绍了使用NLTH的大规模不完美信息游戏研究的集成工具包。 OpenHoldem对这一研究方向做出了三个主要贡献：1）一种标准化评估协议，用于彻底评估不同的NLTH AIS，2）四个公开可用于NLTH AI的强大基线，以及3）一个在线测试平台，具有易于使用的API，用于公共NLTH AI评估。我们已经在Holdem.ia.ac.cn上发布了OpenHoldem，希望它促进对该领域未解决的理论和计算问题的进一步研究，并培养了关键的研究问题，例如对手建模和人类计算机交互式学习。
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research. However, it remains challenging for new researchers to study this problem since there are no standard benchmarks for comparing with existing methods, which seriously hinders further developments in this research area. In this work, we present OpenHoldem, an integrated toolkit for large-scale imperfect-information game research using NLTH. OpenHoldem makes three main contributions to this research direction: 1) a standardized evaluation protocol for thoroughly evaluating different NLTH AIs, 2) four publicly available strong baselines for NLTH AI, and 3) an online testing platform with easy-to-use APIs for public NLTH AI evaluation. We have released OpenHoldem at holdem.ia.ac.cn, hoping it facilitates further studies on the unsolved theoretical and computational issues in this area and cultivate crucial research problems like opponent modeling and human-computer interactive learning.
