2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
Daejin Jo, Sungwoong Kim, Daniel Wontae Nam +4
Episodic count has been widely used to design a simple yet effective intrinsic motivation for reinforcement learning with a sparse reward. However, the use of episodic count in a h…
cs.CL2022
Selective Token Generation for Few-shot Natural Language Generation
Daejin Jo, Taehwan Kwon, Eun-Sol Kim +1
Natural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its gre…
cs.LG2020
Variational Intrinsic Control Revisited
Taehwan Kwon
In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent.…