날짜 : 2022년 3월 23일 오후 5시
장소 :  https://yonsei.zoom.us/j/9729804081
ID 및 PW : -

• 제목: Unsupervised Skill Discovery

• 초록: In this talk, I will present some of our recent works for unsupervised skill discovery in reinforcement learning, whose goal is to teach agents to acquire inherent skills from environments without any external rewards or supervision. First, I deal with how to make policy gradient (PG) methods invariant to time discretization for control. Second, I propose a novel unsupervised skill discovery method named Information Bottleneck Option Learning (IBOL) that leverages the information bottleneck principle from representation learning. Finally, I discuss Lipschitz-constrained Skill Discovery (LSD), which encourages the agent to discover more diverse, dynamic, and far-reaching skills than previous unsupervised skill discovery methods. All these works are recently published in ICML 2021, NeurIPS 2021 and ICLR 2022.
  

• 약력: Gunhee Kim is an associate professor in the Department of Computer Science and Engineering of Seoul National University from 2015. He was a postdoctoral researcher with Leonid Sigal at Disney Research for one and a half years. He received his PhD in 2013 under supervision of Eric P. Xing from Computer Science Department of Carnegie Mellon University. Prior to starting PhD study in 2009, he earned a master’s degree under supervision of Martial Hebert in Robotics Institute, CMU. His research interests are solving computer vision and web mining problems that emerge from big image data shared online, by developing scalable and effective machine learning and optimization techniques. He is a recipient of 2014 ACM SIGKDD doctoral dissertation award, 2015 Naver New faculty award, and Best Full Paper Runner-up at ACM VRST 2019. Please visit his website for more details: https://vision.snu.ac.kr/gunhee/ .

  
  

• 소속: 서울대학교 컴퓨터공학부