publications

2026

1. RAPID: A Rapid Prototyping Platform for Industrial Automation

   Sunghoon Hong, Junseok Park, Deunsol Yoon, Woohyung Lim, and 2 more authors

   In AAAI 2026 Demonstration Program, 2026

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2. RL-Studio: A System for Multi-Phase Reinforcement Learning Experimentation

   Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Kim Jeonghye, and 8 more authors

   In AAAI 2026 Demonstration Program, 2026

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2025

1. IJCAI Workshop

   

   Enhancing Naphtha Cracking Center Scheduling via Population-Based Multi-Scenario Planning

   Deunsol Yoon^*, Sunghoon Hong^*, Whiyoung Jung^*, Kanghoon Lee, and 1 more author

   In IJCAI 2025 Workshop: Agent AI for Scenario Planning (AgentScen), 2025

   Bib HTML PDF

   @inproceedings{yoon2025enhancing,
     title = {Enhancing Naphtha Cracking Center Scheduling via Population-Based Multi-Scenario Planning},
     author = {Yoon, Deunsol and Hong, Sunghoon and Jung, Whiyoung and Lee, Kanghoon and Lim, Woohyung},
     booktitle = {IJCAI 2025 Workshop: Agent AI for Scenario Planning (AgentScen)},
     year = {2025},
   }

2. ICML

   

   Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning

   Whiyoung Jung^*, Sunghoon Hong^*, Deunsol Yoon^*, Kanghoon Lee, and 1 more author

   In International Conference on Machine Learning (ICML), 2025

   Bib HTML PDF

   @inproceedings{jung2025agent,
     title = {Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning},
     author = {Jung, Whiyoung and Hong, Sunghoon and Yoon, Deunsol and Lee, Kanghoon and Lim, Woohyung},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2025},
   }

3. ICML

   

   Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data

   Jeonghye Kim, Yongjae Shin, Whiyoung Jung, Sunghoon Hong, and 4 more authors

   In International Conference on Machine Learning (ICML), 2025

   Spotlight (2.6%)

   Bib HTML PDF

   @inproceedings{kim2025penalizing,
     title = {Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data},
     author = {Kim, Jeonghye and Shin, Yongjae and Jung, Whiyoung and Hong, Sunghoon and Yoon, Deunsol and Sung, Youngchul and Lee, Kanghoon and Lim, Woohyung},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2025},
     note = {Spotlight (2.6%)},
   }

4. ICML

   

   Online Pre-Training for Offline-to-Online Reinforcement Learning

   Yongjae Shin, Jeonghye Kim, Whiyoung Jung, Sunghoon Hong, and 7 more authors

   In International Conference on Machine Learning (ICML), 2025

   Bib HTML PDF

   @inproceedings{shin2025online,
     title = {Online Pre-Training for Offline-to-Online Reinforcement Learning},
     author = {Shin, Yongjae and Kim, Jeonghye and Jung, Whiyoung and Hong, Sunghoon and Yoon, Deunsol and Jang, Youngsoo and Kim, Geon-Hyeong and Chae, Jongseong and Sung, Youngchul and Lee, Kanghoon and Lim, Woohyung},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2025},
   }

2024

1. AAMAS Workshop

   

   Agent-Oriented Centralized Critic for Asynchronous Multi-Agent Reinforcement Learning

   Sunghoon Hong^*, Whiyoung Jung^*, Deunsol Yoon^*, Kanghoon Lee, and 1 more author

   In AAMAS 2024 Workshop: Adaptive Learning and Agents (ALA), 2024

   Bib HTML

   @inproceedings{hong2024agent,
     title = {Agent-Oriented Centralized Critic for Asynchronous Multi-Agent Reinforcement Learning},
     author = {Hong, Sunghoon and Jung, Whiyoung and Yoon, Deunsol and Lee, Kanghoon and Lim, Woohyung},
     booktitle = {AAMAS 2024 Workshop: Adaptive Learning and Agents (ALA)},
     year = {2024},
   }

2. AAMAS

   

   Naphtha Cracking Center Scheduling Optimization using Multi-Agent Reinforcement Learning

   Sunghoon Hong, Deunsol Yoon, Whiyoung Jung, Jinsang Lee, and 8 more authors

   In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024

   Bib HTML PDF

   @inproceedings{hong2024naphtha,
     title = {Naphtha Cracking Center Scheduling Optimization using Multi-Agent Reinforcement Learning},
     author = {Hong, Sunghoon and Yoon, Deunsol and Jung, Whiyoung and Lee, Jinsang and Yoo, Hyundam and Ham, Jiwon and Jung, Suhyun and Moon, Chanwoo and Jung, Yeontae Jung and Lee, Kanghoon and Lim, Woohyung and others},
     booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
     year = {2024},
   }

2023

1. ICML Workshop

   

   Hierarchical Decomposition Framework for Feasibility-hard Combinatorial Optimization

   Hanbum Ko, Minu Kim, Han-Seul Jeong, Sunghoon Hong, and 4 more authors

   In ICML 2023 Workshop: Sampling and Optimization in Discrete Space (SODS), 2023

   Bib PDF

   @inproceedings{ko2023hierarchical,
     title = {Hierarchical Decomposition Framework for Feasibility-hard Combinatorial Optimization},
     author = {Ko, Hanbum and Kim, Minu and Jeong, Han-Seul and Hong, Sunghoon and Yoon, Deunsol and Park, Youngjoon and Lim, Woohyung and others},
     booktitle = {ICML 2023 Workshop: Sampling and Optimization in Discrete Space (SODS)},
     year = {2023},
   }

2022

1. ICLR

   

   Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning

   Sunghoon Hong, Deunsol Yoon, and Kee-Eung Kim

   In International Conference on Learning Representations (ICLR), 2022

   Bib HTML PDF Code

   @inproceedings{hong2022structure,
     title = {Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning},
     author = {Hong, Sunghoon and Yoon, Deunsol and Kim, Kee-Eung},
     booktitle = {International Conference on Learning Representations (ICLR)},
     year = {2022},
   }

2. NeurIPS Workshop

   

   ReSPack: A Large-Scale Rectilinear Steiner Tree Packing Data Generator and Benchmark

   Kanghoon Lee, Youngjoon Park, Han-Seul Jeong, Sunghoon Hong, and 3 more authors

   In NeurIPS 2022 Workshop: SyntheticData4ML, 2022

   Bib PDF

   @inproceedings{lee2022respack,
     title = {ReSPack: A Large-Scale Rectilinear Steiner Tree Packing Data Generator and Benchmark},
     author = {Lee, Kanghoon and Park, Youngjoon and Jeong, Han-Seul and Hong, Sunghoon and Yoon, Deunsol and Sohn, Sungryull and others},
     booktitle = {NeurIPS 2022 Workshop: SyntheticData4ML},
     year = {2022},
   }

2021

1. ICLR

   

   Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic

   Deunsol Yoon^*, Sunghoon Hong^*, Byung-Jun Lee, and Kee-Eung Kim

   In International Conference on Learning Representations (ICLR), 2021

   Spotlight (5%)

   Bib HTML PDF

   @inproceedings{yoon2021winning,
     title = {Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic},
     author = {Yoon, Deunsol and Hong, Sunghoon and Lee, Byung-Jun and Kim, Kee-Eung},
     booktitle = {International Conference on Learning Representations (ICLR)},
     year = {2021},
     note = {Spotlight (5%)},
   }