11 citations · 16 across the 4 of their papers we have counts for
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cs.LG2023★ 1 cited
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
Taewook Nam, Juyong Lee, Jesse Zhang +3
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback. In o…
cs.LG2022★ 11 cited
Skill-based Meta-Reinforcement Learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch +2
While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real…
cs.LG2019★ 4 cited
Meta Dropout: Learning to Perturb Features for Generalization
Hae Beom Lee, Taewook Nam, Eunho Yang +1
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb training examples to account for test exa…