4 citations · 8 across the 6 of their papers we have counts for
6 papers
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…
Learning Geometry-aware Representations by Sketching
Hyundo Lee, Inwoo Hwang, Hyunsung Go +3
Understanding geometric concepts, such as distance and shape, is essential for understanding the real world and also for many vision tasks. To incorporate such information into a v…
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…