2 papers
cs.LG2024
Preference Alignment with Flow Matching
Minu Kim, Yongsik Lee, Sehyeok Kang +3
We present Preference Flow Matching (PFM), a new framework for preference-based reinforcement learning (PbRL) that streamlines the integration of preferences into an arbitrary clas…
cs.LG2022
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
Mingyu Kim, Jihwan Oh, Yongsik Lee +4
In this paper, we propose a novel benchmark called the StarCraft Multi-Agent Challenges+, where agents learn to perform multi-stage tasks and to use environmental factors without p…