OpenHoldem: An Open Toolkit for Large-Scale Imperfect-Information Game Research

• URL: http://arxiv.org/abs/2012.06168v2
• Date: Sat, 19 Dec 2020 14:23:46 GMT
• Title: OpenHoldem: An Open Toolkit for Large-Scale Imperfect-Information Game Research
• Authors: Kai Li, Hang Xu, Meng Zhang, Enmin Zhao, Zhe Wu, Junliang Xing, Kaiqi Huang
• Abstract summary: OpenHoldem is 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) three publicly available strong baselines for NLTH AI, and 3) an online testing platform with easy-to-use APIs for public NLTH AI evaluation.
• Score: 82.09426894653237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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) three 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 http://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, large-scale equilibrium-finding, and human-computer interactive learning.

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