{"title":"Reinforcement Learning","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_16","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"364 1","pages":""},"PeriodicalIF":65.3000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations and Trends in Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-030-65900-4_16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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Each issue of Foundations and Trends® in Machine Learning comprises a monograph of at least 50 pages written by research leaders in the field. We aim to publish monographs that provide an in-depth, self-contained treatment of topics where there have been significant new developments. Typically, this means that the monographs we publish will contain a significant level of mathematical detail (to describe the central methods and/or theory for the topic at hand), and will not eschew these details by simply pointing to existing references. Literature surveys and original research papers do not fall within these aims.
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