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cs.LG2025
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning
Junseok Park, Hyeonseo Yang, Min Whoo Lee +3
Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biolog…
cs.LG2024
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning
Junseok Park, Yoonsung Kim, Hee Bin Yoo +5
Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Ins…
cs.LG2024
DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning
Won-Seok Choi, Hyundo Lee, Dong-Sig Han +3
Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources. On the other hand, the direct use of raw dat…