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.NE2022
Generative Evolutionary Strategy For Black-Box Optimizations
Changhwi Park
Many scientific and technological problems are related to optimization. Among them, black-box optimization in high-dimensional space is particularly challenging. Recent neural netw…