2019-12-04
org.kosen.entty.User@63a79d2d
노형미(october18)
- 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
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