Machine Learning & Intelligent Control Lab.

기계학습 및 지능형제어 연구실

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기계학습 및 지능형제어 연구실

The research of our group focuses on the development of breakthrough machine learning (ML) algorithms, theoretical improvements based on mathematics, and real-world ML applications for industrial automation. In order to conduct influential research in the core areas of artificial intelligence (AI), we mainly consider the following research topics:

Reinforcement Learning: We aim to find the optimal control policy based on the reward given by environments to optimally control robots or systems in real-world.
Especially, to consider various situations in real-world systems, we focuses on the following research fields in reinforcement learning.
- Offline Reinforcement Learning
- Domain Adaptation/Imitation Learning
- Multi-Agent Reinforcement Learning
- Meta Reinforcement Learning
- Robust/Safe Learning
- Statistical Learning
- Intelligent Control Systems

Major research field

Reinforcement Learning, Offline RL, Domain Adaptation/Imitation Learning, Multi-Agent RL, Meta RL, Robust/Safe Learning, Intelligent Control Systems

Desired field of research

Real-world AI Application, General Machine Learning, Intelligent Control Systems

Research Keywords and Topics

- Offline Reinforcement Learning
Offline-RL learns policy by utilizing only the experience it gathers without additional interaction with the environment. There are still some challenges, such as distribution shifts and out-of-distribution problem.

- Domain Adaptation/Imitation Learning
Imitation learning is a branch of research aiming to apply reinforcement learning to real-life scenarios, focusing on learning policies that mimic the actions of experts. Recently, there has been progress in research on Domain Adaptation and Cross-Domain studies, enabling imitation of actions from experts in different domains,

- Multi-Agent Reinforcement Learning
In multi-agent RL, multiple agents aim to learn policies that would maximize the expected return from a shared environment. Coordination among the agents is essential for achieving this goal as the agents effect themselves as learning progresses.

Research Publications

* indicates corresponding author

- Proceedings of the AAAI conference on artificial intelligence (AAAI), “FoX: Formation-aware exploration in multi-agent reinforcement learning,” Yonghyeon Jo, Sunwoo Lee, Junghyuk Yum, Seungyul Han*, Vancouver, Canada, Feb. 2024.

- The 37th Conference on Neural Information Processing Systems (NeurIPS) 2023, “Domain Adaptive Imitation Learning with Visual Observtation,” Sungho Choi, Seungyul Han*, Woojun Kim, Jongseong Chae, Whiyoung Jung, Youngchul Sung, New Orleans, LA,USA, Dec. 2023.

- The 39th International Conference on Machine Learning (ICML), “Robust imitation learning against variations in environment dynamics,” Jongseong Chae, Seungyul Han*, Whiyoung Jung, Myungsik Cho, Sungho Choi, and Youngchul Sung, Jul. 2022.

Patents

- “시각적 관찰데이터를 활용한 도메인 적응형 모방학습 방법 및 시스템,” Youngchul Sung, Sungho Choi, Woojun Kim, Jongseong Chae, Whiyoung Jung, Seungyul Han, application number: 10-2023-0021570.

- “환경역학 변화에 강인한 모방학습 방법 및 시스템,” Youngchul Sung, Jongseong Chae, Whiyoung Jung, Myungsik Cho, Sungho Choi, Seungyul Han, application number: 10-2023-0021569.

국가과학기술표준분류

  • EE. 정보/통신
  • EE01. 정보이론
  • EE0108. 인공지능

국가기술지도분류

  • 정보-지식-지능화 사회 구현
  • 012300. 인공지능/지능로봇 기술

녹색기술분류

  • 녹색기술관련 과제 아님
  • 녹색기술관련 과제 아님
  • 999. 녹색기술 관련과제 아님

6T분류

  • IT 분야
  • 정보처리 시스템 및 S/W
  • 010314. 신호처리기술(영상/음성처리/인식/합성)