My research interest lies on applying RL algorithms to challenging tasks where reward specification is burdensome. To this end, I am focusing on designing RL algorithms to tackle practical and challenging scenarios like unseen novel environments and environments without well-shaped rewards. I am also broadly interested in RL researches including RL with representation learning, language‐conditioned RL, and offline RL.
Prior to my graduate studies, I was a machine learning engineer at Recommendation Team of Kakao. Before that, I completed my BS in Computer Science at KAIST.
|Sep 22, 2023||ARP has been accepted to NeurIPS 2023. See you in New Orleans !|
|Jun 19, 2023||I will attend ICML 2023 in person for presenting workshop paper (ARP). Feel free to contact to meet or chat in Hawaii, USA .|
|Jan 21, 2023||Preference Transformer is accepted to ICLR2023 . Hope to see you in Kigali, Rwanda !|
- Guide Your Agent with Adaptive Multimodal RewardsIn Conference on Neural Information Processing Systems (NeurIPS), 2023Previously accepted to ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems
- Preference Transformer: Modeling Human Preferences using Transformers for RLIn International Conference on Learning Representations (ICLR), 2023(*: equal contribution)
- Dynamics-Augmented Decision Transformer for Offline Dynamics GeneralizationIn Neural Information Processing Systems Workshop on Offline Reinforcement Learning, 2022(*: equal contribution)
Recommendation Team, Kakao
Machine Learning Engineer (Dec 2020 ~ Feb 2022)
Data Science Group, Institute of Basic Science
Resarch Intern advised by Prof. Meeyoung Cha (Jul 2019 - Nov 2020)
Dean's List, KAIST Department of Engineering, 2019
Recipient ($5,000), Line Scholarship, 2019
Recipient, National Science and Engineering Scholarship, Korea Ministry of Science and ICT, 2017 - 2019
Recipient ($3,000), Kwanjeong Scholarship, 2017