← Authors

Youngchul Sung

24 papers · 2019–2025 · 5 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (6) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (36) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (86) ⚑ Prolific Year (5) πŸ’Ž Century Club (24) πŸ”₯ Unstoppable (7) πŸš€ Conference Pioneer

Conferences

ICML (11) NIPS (6) ICLR (4) AAAI (2) EMNLP (1)

Top co-authors

Woojun Kim (9) Whiyoung Jung (7) Jeonghye Kim (7) Myungsik Cho (6) Jongeui Park (5) Seungyul Han (5) Giseung Park (4) Suyoung Lee (4) Yongjae Shin (3) Jongseong Chae (3)

Keywords

reinforcement learning (4) multi-agent reinforcement learning (3) policy learning (2) sample efficiency (2) importance sampling (2) entropy regularization (2) deep reinforcement learning (2) visual observation (1) domain generalization (1) offline reinforcement learning (1) policy optimization (1) domain adaptation (1) ensemble learning (1) feature extraction (1) imitation learning (1) supervised learning (1) constrained reinforcement learning (1) policy gradient (1) decision making (1) risk management (1)

Papers

Online Pre-Training for Offline-to-Online Reinforcement Learning ICML 2025
ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection EMNLP 2025
Reward Dimension Reduction for Scalable Multi-Objective Reinforcement Learning ICLR 2025
ARS: Adaptive Reward Scaling for Multi-Task Reinforcement Learning ICML 2025
Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data ICML 2025
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making ICLR 2024
Adaptive $Q$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning NIPS 2024
Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling ICML 2024
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm ICML 2024
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents NIPS 2023
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework ICML 2023
An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning ICML 2023
Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition NIPS 2023
Domain Adaptive Imitation Learning with Visual Observation NIPS 2023
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning AAAI 2022
Robust Imitation Learning against Variations in Environment Dynamics ICML 2022
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer ICML 2022
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability NIPS 2022
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration ICML 2021
Communication in Multi-Agent Reinforcement Learning: Intention Sharing ICLR 2021
A Max-Min Entropy Framework for Reinforcement Learning NIPS 2021
Population-Guided Parallel Policy Search for Reinforcement Learning ICLR 2020
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning AAAI 2019
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning ICML 2019