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Owning to the unremitting efforts by a few institutes, significant progresshas recently been made in designing superhuman AIs in No-limit Texas Hold'em(NLTH), the primary testbed for large-scale imperfect-information gameresearch. However, it remains challenging for new researchers to study thisproblem since there are no standard benchmarks for comparing with existingmethods, which seriously hinders further developments in this research area. Inthis work, we present OpenHoldem, an integrated toolkit for large-scaleimperfect-information game research using NLTH. OpenHoldem makes three maincontributions to this research direction: 1) a standardized evaluation protocolfor thoroughly evaluating different NLTH AIs, 2) four publicly available strongbaselines for NLTH AI, and 3) an online testing platform with easy-to-use APIsfor public NLTH AI evaluation. We have released OpenHoldem at holdem.ia.ac.cn,hoping it facilitates further studies on the unsolved theoretical andcomputational issues in this area and cultivate crucial research problems likeopponent modeling and human-computer interactive learning.

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