{"title":"面向计算机视觉和自然语言处理的深度强化学习研究进展","authors":"Caiming Xiong","doi":"10.1145/3134421.3137039","DOIUrl":null,"url":null,"abstract":"Deep reinforcement learning is considered as a way of building autonomous system with a higher level understanding of the world and would revolutionize the field of AI. Recently, some researchers have made many progresses such as learning to play video games like Atari, learning control policy for robots from camera input. In this talk, we begin with general introduction of deep reinforcement learning algorithms, including policy optimization, deep Qlearning, then we will highlight the progresses that have achieved in Vision and NLP via DRL.","PeriodicalId":209776,"journal":{"name":"Proceedings of the 2017 Workshop on Recognizing Families In the Wild","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Recent Progress in Deep Reinforcement Learning for Computer Vision and NLP\",\"authors\":\"Caiming Xiong\",\"doi\":\"10.1145/3134421.3137039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep reinforcement learning is considered as a way of building autonomous system with a higher level understanding of the world and would revolutionize the field of AI. Recently, some researchers have made many progresses such as learning to play video games like Atari, learning control policy for robots from camera input. In this talk, we begin with general introduction of deep reinforcement learning algorithms, including policy optimization, deep Qlearning, then we will highlight the progresses that have achieved in Vision and NLP via DRL.\",\"PeriodicalId\":209776,\"journal\":{\"name\":\"Proceedings of the 2017 Workshop on Recognizing Families In the Wild\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 Workshop on Recognizing Families In the Wild\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3134421.3137039\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 Workshop on Recognizing Families In the Wild","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3134421.3137039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recent Progress in Deep Reinforcement Learning for Computer Vision and NLP
Deep reinforcement learning is considered as a way of building autonomous system with a higher level understanding of the world and would revolutionize the field of AI. Recently, some researchers have made many progresses such as learning to play video games like Atari, learning control policy for robots from camera input. In this talk, we begin with general introduction of deep reinforcement learning algorithms, including policy optimization, deep Qlearning, then we will highlight the progresses that have achieved in Vision and NLP via DRL.