4 citations · 13 across the 13 of their papers we have counts for
7 papers
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…
Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning
Min Whoo Lee, Kibeom Kim, Soo Wung Shin +2
Applying multi-agent reinforcement learning methods to realistic settings is challenging as it may require the agents to quickly adapt to unexpected situations that are rarely or n…
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…
Visual Hindsight Self-Imitation Learning for Interactive Navigation
Kibeom Kim, Kisung Shin, Min Whoo Lee +3
Interactive visual navigation tasks, which involve following instructions to reach and interact with specific targets, are challenging not only because successful experiences are v…
L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement Learning
Kibeom Kim, Hyundo Lee, Min Whoo Lee +3
Tasks that involve interaction with various targets are called multi-target tasks. When applying general reinforcement learning approaches for such tasks, certain targets that are…
On the Importance of Critical Period in Multi-stage Reinforcement Learning
Junseok Park, Inwoo Hwang, Min Whoo Lee +4
The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plastic…
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
Kibeom Kim, Min Whoo Lee, Yoonsung Kim +3
Learning in a multi-target environment without prior knowledge about the targets requires a large amount of samples and makes generalization difficult. To solve this problem, it is…