Hello! I am a researcher working with Professor Honglak Lee at LG AI Research Center, Ann Arbor. Before that, I received my PhD in Computer Science and Engineering at Seoul National University and worked as a member of Vision & Learning Lab.

My research interests mainly lie in deep reinforcement learning, discovery and abstraction of behaviors at scale, and generalization of learned behaviors to new tasks or domains.



Constrained GPI for Zero-Shot Transfer in Reinforcement Learning
Jaekyeom Kim, Seohong Park, Gunhee Kim
NeurIPS 2022
[paper] [arxiv] [talk] [code]
Lipschitz-constrained Unsupervised Skill Discovery
Seohong Park, Jongwook Choi*, Jaekyeom Kim*, Honglak Lee, Gunhee Kim (*equal contribution)
ICLR 2022
[paper] [arxiv] [project] [code]
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods
Seohong Park, Jaekyeom Kim, Gunhee Kim
NeurIPS 2021
[paper] [appx] [arxiv] [talk] [code]
Unsupervised Skill Discovery with Bottleneck Option Learning
Jaekyeom Kim*, Seohong Park*, Gunhee Kim (*equal contribution)
ICML 2021
[paper] [appx] [arxiv] [talk] [code]
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
Jaekyeom Kim, Minjung Kim, Dongyeon Woo, Gunhee Kim
ICLR 2021
[paper] [arxiv] [talk] [code]
Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot Learning
Jaekyeom Kim, Hyoungseok Kim, Gunhee Kim
ECCV 2020 (Oral)
[paper] [appx] [talk] [code]
EMI: Exploration with Mutual Information
Hyoungseok Kim*, Jaekyeom Kim*, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song (*equal contribution)
ICML 2019 (Long talk)
[paper] [supp] [arxiv] [talk] [code]

Honors & Awards