Knowledge Commons of Institute of Automation,CAS
L2E: Learning to Exploit Your Opponent | |
Wu Zhe1,2; Li Kai1,2; Xu Hang1,2; Zang Yifan1,2; An Bo3; Xing Junliang4 | |
2022-05 | |
会议名称 | International Joint Conference on Neural Networks |
会议日期 | 2022.07.18-2022.07.23 |
会议地点 | 意大利 帕多瓦 |
摘要 | Opponent modeling is essential to exploit suboptimal opponents in strategic interactions. Most previous works focus on building explicit models to predict the opponents' styles or strategies, which require a large amount of data to train the model and lack adaptability to unknown opponents. In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling. L2E acquires the ability to exploit opponents through a few interactions with different opponents during training of a neural network and can quickly adapt to new opponents with unknown styles during testing. To automatically produce challenging and diverse opponents for training, we further present a novel opponent strategy generation algorithm. We evaluate L2E on two poker games and one grid soccer game, which are the commonly used benchmarks for opponent modeling. Comprehensive experimental results indicate that L2E rapidly adapts to diverse styles of unknown opponents. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48788 |
专题 | 复杂系统认知与决策实验室_智能系统与工程 |
通讯作者 | Xing Junliang |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.School of Computer Science and Engineering, Nanyang Technological University 4.Department of Computer Science and Technology, Tsinghua University |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wu Zhe,Li Kai,Xu Hang,et al. L2E: Learning to Exploit Your Opponent[C],2022. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Learning to Exploit (5676KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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