Sunghoon Hong

@inproceedings{yoon2026designing,
  title = {Designing Observation and Action Models for Efficient Reinforcement Learning with {LLMs}},
  author = {Yoon, Deunsol and Hong, Sunghoon and Jung, Whiyoung and Park, Junseok and Kim, Geon-Hyeong and Lim, Woohyung and Lee, Soonyoung and Kim, Seung Hwan and Lee, Kanghoon},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
}

@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},
}

@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%)},
}

@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},
}

@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},
}

@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%)},
}

@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},
}

@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},
}

@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},
}

@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},
}

@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},
}