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

doi:10.1109/TNNLS.2023.3280186

CORC > 自动化研究所 > 中国科学院自动化研究所 > 融合创新中心 > 决策指挥与体系智能

	OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
	Li, Kai 1,3; Xu, Hang 1,3; Zhao, Enmin 1,3; Wu, Zhe 1,3; Xing, Junliang 2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2023-06-14
页码	15
关键词	Artificial intelligence (AI) benchmark imperfect-information game Nash equilibrium no-limit Texas hold'em (NLTH)
ISSN号	2162-237X
DOI	10.1109/TNNLS.2023.3280186
通讯作者	Xing, Junliang(jlxing@tsinghua.edu.cn)
英文摘要	Owing to the unremitting efforts from a few institutes, researchers have recently made significant progress in designing superhuman artificial intelligence (AI) 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 hinders further developments in this research area. This work presents OpenHoldem, an integrated benchmark 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 will publicly release OpenHoldem and hope it facilitates further studies on the unsolved theoretical and computational issues in this area and cultivates crucial research problems like opponent modeling and human-computer interactive learning.
资助项目	National Key Research and Development Program of China[2022ZD0116401] ; Natural Science Foundation of China[62076238] ; Natural Science Foundation of China[62222606] ; Natural Science Foundation of China[61902402] ; China Computer Federation (CCF)-Tencent Open Fund ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27000000]
WOS关键词	LEVEL ; GO ; POKER ; ALGORITHM ; SHOGI ; CHESS
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001012538800001
资助机构	National Key Research and Development Program of China ; Natural Science Foundation of China ; China Computer Federation (CCF)-Tencent Open Fund ; Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53570]
专题	融合创新中心_决策指挥与体系智能
通讯作者	Xing, Junliang
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Kai,Xu, Hang,Zhao, Enmin,et al. OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:15.
APA	Li, Kai,Xu, Hang,Zhao, Enmin,Wu, Zhe,&Xing, Junliang.(2023).OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA	Li, Kai,et al."OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):15.