{"title":"Markov Decision Process Framework for Control-Based Reinforcement Learning","authors":"Yingdong Lu, Mark S. Squillante, Chai Wah Wu","doi":"10.1145/3626570.3626585","DOIUrl":null,"url":null,"abstract":"For many years, reinforcement learning (RL) has proven to be very successful in solving a wide variety of learning and decision making under uncertainty (DMuU) problems, including those related to game playing and robotic control. Many different RL approaches, with varying levels of success, have been developed to address these problems.","PeriodicalId":35745,"journal":{"name":"Performance Evaluation Review","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance Evaluation Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3626570.3626585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
For many years, reinforcement learning (RL) has proven to be very successful in solving a wide variety of learning and decision making under uncertainty (DMuU) problems, including those related to game playing and robotic control. Many different RL approaches, with varying levels of success, have been developed to address these problems.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
