{"title":"作为分层学习环境的战争象棋","authors":"Shang Jiang, Wenxia Wei, Yanlin Wu, Rui Tang, Qingquan Feng, Daogang Ji","doi":"10.1109/ISCID51228.2020.00089","DOIUrl":null,"url":null,"abstract":"This paper introduces GWCLE (General War Chess Learning Environment), a general machine learning environment based on hexagonal wargaming. Hexagonal war chess, when utilized as machine learning challenge, is naturally a multi-agent problem with the intelligent interaction of human or machine. The GWCLE supports hybrid engine, allowing credible simulation for kinds of war chess, which provides hierarchical training framework for massive agents control problem. The agent can be trained with designated level of war chess data and transferred bottom-up or top-down. For training on the whole deduction, we build the database to store refined replay data. Our framework is able to support agents to be trained in tactical and strategic level simultaneously. GWCLE offers a hierarchical perspective of the war chess simulation, allowing researchers controlling the granularity of action and time step.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"War Chess as Hierarchical Learning Environment\",\"authors\":\"Shang Jiang, Wenxia Wei, Yanlin Wu, Rui Tang, Qingquan Feng, Daogang Ji\",\"doi\":\"10.1109/ISCID51228.2020.00089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces GWCLE (General War Chess Learning Environment), a general machine learning environment based on hexagonal wargaming. Hexagonal war chess, when utilized as machine learning challenge, is naturally a multi-agent problem with the intelligent interaction of human or machine. The GWCLE supports hybrid engine, allowing credible simulation for kinds of war chess, which provides hierarchical training framework for massive agents control problem. The agent can be trained with designated level of war chess data and transferred bottom-up or top-down. For training on the whole deduction, we build the database to store refined replay data. Our framework is able to support agents to be trained in tactical and strategic level simultaneously. GWCLE offers a hierarchical perspective of the war chess simulation, allowing researchers controlling the granularity of action and time step.\",\"PeriodicalId\":236797,\"journal\":{\"name\":\"2020 13th International Symposium on Computational Intelligence and Design (ISCID)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 13th International Symposium on Computational Intelligence and Design (ISCID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCID51228.2020.00089\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID51228.2020.00089","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
本文介绍了一种基于六边形兵棋推演的通用机器学习环境GWCLE (General War Chess Learning Environment)。六角形棋作为机器学习挑战,自然是一个人机智能交互的多智能体问题。GWCLE支持混合引擎,可对各种棋类进行可信模拟,为大规模智能体控制问题提供了分层训练框架。代理可以使用指定级别的战争象棋数据进行训练,并自底向上或自顶向下传输。为了对整个演绎进行训练,我们建立了数据库来存储精细化的重播数据。我们的框架能够支持特工同时接受战术和战略层面的培训。GWCLE提供了战争象棋模拟的分层视角,允许研究人员控制行动粒度和时间步长。
This paper introduces GWCLE (General War Chess Learning Environment), a general machine learning environment based on hexagonal wargaming. Hexagonal war chess, when utilized as machine learning challenge, is naturally a multi-agent problem with the intelligent interaction of human or machine. The GWCLE supports hybrid engine, allowing credible simulation for kinds of war chess, which provides hierarchical training framework for massive agents control problem. The agent can be trained with designated level of war chess data and transferred bottom-up or top-down. For training on the whole deduction, we build the database to store refined replay data. Our framework is able to support agents to be trained in tactical and strategic level simultaneously. GWCLE offers a hierarchical perspective of the war chess simulation, allowing researchers controlling the granularity of action and time step.