{"title":"强化学习的复杂性分析及其在机器人中的应用","authors":"Bocheng Li, L. Xia, Qianchuan Zhao","doi":"10.1109/COASE.2017.8256303","DOIUrl":null,"url":null,"abstract":"Reinforcement learning (RL) is a widely adopted theory in machine learning, which aims to handle the optimal decision of intelligent agent interacting with the stochastic dynamic environment. Its origin may come from the motivation of phycological observations since 1960's [1]. It blooms recently as the emerging of large sample data and powerful computation facility, especially the AlphaGo's beat over the human top Go player in 2016 [2].","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Complexity analysis of reinforcement learning and its application to robotics\",\"authors\":\"Bocheng Li, L. Xia, Qianchuan Zhao\",\"doi\":\"10.1109/COASE.2017.8256303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reinforcement learning (RL) is a widely adopted theory in machine learning, which aims to handle the optimal decision of intelligent agent interacting with the stochastic dynamic environment. Its origin may come from the motivation of phycological observations since 1960's [1]. It blooms recently as the emerging of large sample data and powerful computation facility, especially the AlphaGo's beat over the human top Go player in 2016 [2].\",\"PeriodicalId\":445441,\"journal\":{\"name\":\"2017 13th IEEE Conference on Automation Science and Engineering (CASE)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th IEEE Conference on Automation Science and Engineering (CASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COASE.2017.8256303\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2017.8256303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Complexity analysis of reinforcement learning and its application to robotics
Reinforcement learning (RL) is a widely adopted theory in machine learning, which aims to handle the optimal decision of intelligent agent interacting with the stochastic dynamic environment. Its origin may come from the motivation of phycological observations since 1960's [1]. It blooms recently as the emerging of large sample data and powerful computation facility, especially the AlphaGo's beat over the human top Go player in 2016 [2].
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