{"title":"无模型强化学习算法:综述","authors":"Sinan Çalisir, Meltem Kurt PehlIvanoõlu","doi":"10.1109/SIU.2019.8806389","DOIUrl":null,"url":null,"abstract":"This paper aims to provide a comprehensive survey of the reinforcement learning algorithms given in the literature. Especially model-free reinforcement learning algorithms are given in details and the differences of these algorithms are handled. Finally, some open problems in reinforcement learning are presented for future researches.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Model-Free Reinforcement Learning Algorithms: A Survey\",\"authors\":\"Sinan Çalisir, Meltem Kurt PehlIvanoõlu\",\"doi\":\"10.1109/SIU.2019.8806389\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to provide a comprehensive survey of the reinforcement learning algorithms given in the literature. Especially model-free reinforcement learning algorithms are given in details and the differences of these algorithms are handled. Finally, some open problems in reinforcement learning are presented for future researches.\",\"PeriodicalId\":326275,\"journal\":{\"name\":\"2019 27th Signal Processing and Communications Applications Conference (SIU)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 27th Signal Processing and Communications Applications Conference (SIU)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2019.8806389\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 27th Signal Processing and Communications Applications Conference (SIU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2019.8806389","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Model-Free Reinforcement Learning Algorithms: A Survey
This paper aims to provide a comprehensive survey of the reinforcement learning algorithms given in the literature. Especially model-free reinforcement learning algorithms are given in details and the differences of these algorithms are handled. Finally, some open problems in reinforcement learning are presented for future researches.
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