1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 1 cited
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Giseung Park, Woohyeon Byeon, Seongmin Kim +3
In this paper, we consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals. We approach the problem with a max-min…
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…