Cerberus:将监督和强化学习技术应用于夺旗游戏

Ahmed S. Hefny, Ayat A. Hatem, Mahmoud Shalaby, Amir Atiya
{"title":"Cerberus:将监督和强化学习技术应用于夺旗游戏","authors":"Ahmed S. Hefny, Ayat A. Hatem, Mahmoud Shalaby, Amir Atiya","doi":"10.1609/aiide.v4i1.18694","DOIUrl":null,"url":null,"abstract":"Applying machine learning techniques to real-time computer games is an active research field. In this paper we present Cerberus, a machine learning framework for team-based Capture The Flag (CTF) games. This framework utilizes reinforcement learning to select high-level actions that achieve best team behaviour and utilizes neural networks to control fighting behaviour of team individuals. Our proposed framework also combines waypoints and influence maps for effective path planning","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cerberus: Applying Supervised and Reinforcement Learning Techniques to Capture the Flag Games\",\"authors\":\"Ahmed S. Hefny, Ayat A. Hatem, Mahmoud Shalaby, Amir Atiya\",\"doi\":\"10.1609/aiide.v4i1.18694\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Applying machine learning techniques to real-time computer games is an active research field. In this paper we present Cerberus, a machine learning framework for team-based Capture The Flag (CTF) games. This framework utilizes reinforcement learning to select high-level actions that achieve best team behaviour and utilizes neural networks to control fighting behaviour of team individuals. Our proposed framework also combines waypoints and influence maps for effective path planning\",\"PeriodicalId\":249108,\"journal\":{\"name\":\"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment\",\"volume\":\"74 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1609/aiide.v4i1.18694\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aiide.v4i1.18694","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

将机器学习技术应用于实时电脑游戏是一个活跃的研究领域。在本文中,我们介绍了Cerberus,这是一个基于团队的Capture The Flag (CTF)游戏的机器学习框架。该框架利用强化学习选择实现最佳团队行为的高级行动,并利用神经网络控制团队个人的战斗行为。我们提出的框架还结合了路点和影响图,以实现有效的路径规划
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cerberus: Applying Supervised and Reinforcement Learning Techniques to Capture the Flag Games
Applying machine learning techniques to real-time computer games is an active research field. In this paper we present Cerberus, a machine learning framework for team-based Capture The Flag (CTF) games. This framework utilizes reinforcement learning to select high-level actions that achieve best team behaviour and utilizes neural networks to control fighting behaviour of team individuals. Our proposed framework also combines waypoints and influence maps for effective path planning
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信