OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research

11 Dec 2020  ·  Kai Li, Hang Xu, Enmin Zhao, Zhe Wu, Junliang Xing ·

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, 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.

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