Hyunseung Kim

logo_uni_tue          logo_uni_tue

I am a Ph.D. student at Data and Visual Analytics Lab (DAVIAN-LAB), advised by Prof. Jaegul Choo at Korea Advanced Institute of Science and Technology (KAIST). I received my B.S. in Computer Science at Korea University in 2021.

Currently, I am a reinforcement learning research intern at KRAFTON, mentored by Kangwook Lee.

My primary research objective is to develop intelligent agents that are capable of autonomously acquiring knowledge and meaningful behaviors through interactions within the environment. I am currently addressing this challenge by integrating unsupervised reinforcement learning algorithms with prior data to improve learning efficiency and ensure alignment with human intentions.

Email  /  CV  /  Google Scholar  /  Linkedin  /  Github

profile photo

News


Publications

preprint2024dodont

Do’s and Don’ts: Learning Desirable Skills with Instruction Videos
Hyunseung Kim, Byungkun Lee, Hojoon Lee, Dongyoon Hwang, Donghu Kim, Jaegul Choo
NeurIPS'24.
project page / paper

We present DoDont, an instruction-based skill discovery algorithm designed to combine human intention with unsupervised skill discovery. DoDont learns diverse behvaiors while following the behaviors in "do" videos while avoiding the behaviors in "don't" videos.

icml2024hnt

Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks
Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle
ICML'24.
paper / code

To allow the network to continually adapt and generalize, we introduce Hare and Tortoise architecture, inspired by the complementary learning system of the human brain.

icml2024coin

A Simple Convolution INjector for Vision Transformer: Towards Effective Adaptation in Visuo-Motor Control
Dongyoon Hwang*, Byungkun Lee*, Hojoon Lee, Hyunseung Kim, Jaegul Choo
ICML'24.
paper

We introduce CoIn, a lightweight and simple add-on module, which effectively adapts pretrained Vision Transformers for visuo-motor control.

neurips2023disco-dance

DISCO-DANCE: Learning to Discover Skills through Guidance
Hyunseung Kim*, Byungkun Lee*, Hojoon Lee, Dongyoon Hwang, Sejik Park, Kyushik Min, Jaegul Choo
NeurIPS'23.
project page / paper / code / poster

We introduce DISCO-DANCE, a skill discovery algorithm focused on learning diverse, task-agnostic behaviors. DISCO-DANCE addresses the common limitation of exploration in skill discovery algorithms through explicit guidance.

neurips2023plastic

PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
Hojoon Lee*, Hanseul Cho*, Hyunseung Kim*, Daehoon Gwak, Joonkee Kim, Jaegul Choo SeYoung Yun, Chulhee Yun,
NeurIPS'23.
paper / code / slide / poster /

For sample-efficient RL, the agent needs to quickly adapt to various inputs (input plasticity) and outputs (label plasticity). We present PLASTIC, which maintains both input and label plasticity by identifying smooth local minima and preserving gradient flow.

www2022draftrec

DraftRec: Personalized Draft Recommendation for Winning in MOBA Games
Hojoon Lee*, Dongyoon Hwang*, Hyunseung Kim, Byungkun Lee, Jaegul Choo
WWW'22.
paper / code / poster / Bibtex

We gathered data from 280,000 matches played by the top 0.3% rank players in Korea for League of Legends. From this, we developed DraftRec, a personalized champion recommendation system aimed at maximizing players' win rates.

Template based on Hojoon's website.