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一种用于两人零和博弈对手适应的元策略演化学习算法

吴哲 李凯 徐航 兴军亮

吴哲, 李凯, 徐航, 兴军亮. 一种用于两人零和博弈对手适应的元策略演化学习算法. 自动化学报, 2022, 48(x): 1−12 doi: 10.16383/j.aas.c211003
引用本文: 吴哲, 李凯, 徐航, 兴军亮. 一种用于两人零和博弈对手适应的元策略演化学习算法. 自动化学报, 2022, 48(x): 1−12 doi: 10.16383/j.aas.c211003
Wu Zhe, Li Kai, Xu Hang, Xing Jun-Liang. A meta-evolutionary learning algorithm for opponent adaptation in two-player zero-sum games. Acta Automatica Sinica, 2022, 48(x): 1−12 doi: 10.16383/j.aas.c211003
Citation: Wu Zhe, Li Kai, Xu Hang, Xing Jun-Liang. A meta-evolutionary learning algorithm for opponent adaptation in two-player zero-sum games. Acta Automatica Sinica, 2022, 48(x): 1−12 doi: 10.16383/j.aas.c211003

一种用于两人零和博弈对手适应的元策略演化学习算法

doi: 10.16383/j.aas.c211003
基金项目: 国家重点研发计划(2020AAA0103401), 国家自然科学基金(62076238, 61902402), 中国科学院战略性先导研究项目(XDA27000000), CCF-腾讯犀牛鸟基金(RAGR20200104)资助
详细信息
    作者简介:

    吴哲:中国科学院自动化研究所智能系统与工程研究中心硕士研究生. 2019年获得山东大学工学学士学位. 主要研究方向为计算机博弈, 强化学习. E-mail: wuzhe2019@ia.ac.cn

    李凯:中国科学院自动化研究所智能系统与工程研究中心副研究员. 2018年获得中国科学院自动化研究所模式识别与智能系统博士学位. 主要研究方向为计算机博弈, 强化学习. E-mail: kai.li@ia.ac.cn

    徐航:中国科学院自动化研究所智能系统与工程研究中心硕士研究生. 2020年获得武汉大学工学学士学位. 主要研究方向为计算机博弈, 强化学习. E-mail: xuhang2020@ia.ac.cn

    兴军亮:中国科学院自动化研究所智能系统与工程研究中心研究员. 2012年获得清华大学计算机科学与技术系博士学位. 主要研究方向为计算机博弈. 本文通信作者. E-mail: jlxing@nlpr.ia.ac.cn

A Meta-evolutionary Learning Algorithm for Opponent Adaptation in Two-player Zero-sum Games

Funds: Supported by National Key Research and Development Program of China (2020AAA0103401), National Natural Science Foundation of China (62076238, 61902402), Strategic Priority Research Program of Chinese Academy of Sciences (XDA27000000), and CCF-Tencent Open Research Fund (RAGR20200104)
More Information
    Author Bio:

    WU Zhe Master student at the Center for Research on Intelligent System and Engineering, Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree in engineering from Shandong University in 2019. His research interest covers computer game and reinforcement learning

    LI Kai Associate professor at the Center for Research on Intelligent System and Engineering, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in pattern recognition and intelligent system from Institute of Automation, Chinese Academy of Sciences in 2018. His research interest covers computer game and reinforcement learning

    XU Hang Master student at the Center for Research on Intelligent System and Engineering, Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree in engineering from Wuhan University in 2020. His research interest covers computer game and reinforcement learning

    XING Jun-Liang Professor at the Center for Research on Intelligent System and Engineering, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in Department of computer science and technology from Tsinghua University in 2012. His main research interest is computer game. Corresponding author of this paper

  • 摘要: 围绕两人零和博弈所开展的一系列研究, 近年来在围棋、德州扑克等问题中取得了里程碑式的突破. 现有的两人零和博弈求解方案大多在理性对手的假设下围绕纳什均衡解开展, 是一种力求不败的保守型策略, 但在实际博弈中由于对手非理性等原因并不能保证收益最大化. 对手建模为最大化博弈收益提供了一种新途径, 但仍存在建模困难等问题. 结合元学习的思想提出了一种能够快速适应对手策略的元策略演化学习求解框架. 在训练阶段, 首先通过种群演化的方法不断生成风格多样化的博弈对手作为训练数据, 然后利用元策略更新方法来调整元模型的网络权重, 使其获得快速适应的能力. 在Leduc扑克、两人有限注德州扑克和RoboSumo上的大量实验结果表明, 本算法能够有效克服现有方法的弊端, 实现针对未知风格对手的快速适应, 从而为两人零和博弈收益最大化求解提供了一种新思路.
  • 图  1  本文提出的元模型训练方法

    Fig.  1  The meta-model’s training process

    图  2  元模型与基线算法在RoboSumo中的对比

    Fig.  2  The average win-rates in RoboSumo

    图  3  消融实验

    Fig.  3  Ablation study

    图  5  超参数设置对模型性能影响

    Fig.  5  Effect of hyperparameter settings

    图  4  种群演化模块生成的对手策略

    Fig.  4  Visualization of the styles of the strategies

    表  1  不同环境下的实验参数设置

    Table  1  Hyperparameters settings

    参数LeducLHERoboSumo
    状态空间3672120
    动作空间448
    网络尺寸[64, 64][128, 128][128, 128]
    训练步长$\alpha$0.1000.0500.003
    训练步长$\beta$0.01000.01000.0006
    折扣系数$\gamma$0.9900.9950.995
    梯度更新步数111
    种群规模101010
    精英比例$\vartheta$0.20.40.4
    变异率0.30.10.2
    变异强度0.10.10.1
    测试更新步长0.1000.050 0.001
    测试更新步数333
    评估局数5010050
    存储资源(GB)~0.3 ~2.0~1.5
    迭代次数(T)100400300
    下载: 导出CSV

    表  2  元模型与基线算法在Leduc环境中的对比

    Table  2  The average return of our method and baseline methods in Leduc

    方法Random对手Call对手Bluff对手CFR对手NFSP对手
    本文算法1.359±0.0230.646±0.0690.576±0.043−0.162±0.0320.325±0.096
    CFR算法0.749±0.0140.364±0.0100.283±0.0280.010±0.0240.144±0.007
    DRON算法1.323±0.0140.418±0.0110.409±0.052−0.347±0.0310.212±0.080
    EOM算法1.348±0.0150.635±0.0070.444±0.024−0.270±0.042−0.012±0.023
    NFSP算法0.780±0.0190.132±0.0240.029±0.022−0.412±0.0400.011±0.027
    MAML算法1.372±0.0280.328±0.0130.323±0.044−0.409±0.0100.089±0.051
    本文算法+PPO1.353±0.0110.658±0.0050.555±0.017−0.159±0.0410.314±0.012
    本文算法-EA0.994±0.0420.611±0.0210.472±0.038−0.224±0.0160.203±0.029
    EA算法0.535±0.1640.422±0.1080.366±0.113−0.365±0.0940.189±0.102
    Oracle1.373±0.0070.662±0.0140.727±0.012−0.089±0.0160.338±0.041
    下载: 导出CSV

    表  3  元模型与基线算法在LHE环境中的对比

    Table  3  The average return of our method and baseline methods in LHE

    方法Random对手LA对手TA对手LP对手
    本文算法2.594±0.0890.335±0.0120.514±0.0310.243±0.102
    DRON算法2.131±0.672−0.609±0.1760.294±0.0570.022±0.028
    EOM算法2.555±0.020−0.014±0.0130.237±0.0230.144±0.128
    NFSP算法1.342±0.033−0.947±0.012−0.352±0.0940.203±0.089
    MAML算法2.633±0.0350.037±0.0470.231±0.0570.089±0.051
    本文算法+PPO2.612±0.0580.327±0.0110.478±0.0420.246±0.070
    本文算法-EA2.362±0.0230.185±0.0490.388±0.0120.119±0.015
    EA算法2.193±0.1580.096±0.0870.232±0.0970.091±0.009
    ORACLE2.682±0.0330.513±0.0090.624±0.0110.270±0.026
    下载: 导出CSV
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  • 收稿日期:  2021-10-22
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