233 citations · 785 across the 38 of their papers we have counts for
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cs.LG2021
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
Giseung Park, Sungho Choi, Youngchul Sung
This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems. Rather than compressing sequential information at every tim…
cs.LG2021★ 5 cited
A Max-Min Entropy Framework for Reinforcement Learning
Seungyul Han, Youngchul Sung
In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum e…