동향

[전자통신동향분석] 역강화학습 기술 동향

분야

정보/통신

발행기관

한국전자통신연구원

발행일

2019.12.01

URL


초록
Recently, reinforcement learning (RL) has expanded from the research phase of the virtual simulation environment to a wide range of applications, such as autonomous driving, natural language processing, recommendation systems, and disease diagnosis. However, RL is less likely to be used in these complex real-world environments. In contrast, inverse reinforcement learning (IRL) can obtain optimal policies in various situations; furthermore, it can use expert demonstration data to achieve its target task. In particular, IRL is expected to be a key technology for artificial general intelligence research that can successfully perform human intellectual tasks. In this report, we briefly summarize various IRL techniques and research directions.
 
저자
이상광지능형지식콘텐츠연구실sklee@etri.re.kr
김대욱지능형지식콘텐츠연구실dooroomie@etri.re.kr
장시환지능형지식콘텐츠연구실jjangshan@etri.re.kr
양성일지능형지식콘텐츠연구실siyang@etri.re.kr
리포트 평점  
해당 콘텐츠에 대한 회원님의 소중한 평가를 부탁드립니다.
0.0 (0개의 평가)
평가하기
등록된 댓글이 없습니다.