2 papers
cs.LG2026
UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning
Mohamed Nabail, Leo Kaixuan Cheng, Jingmin Wang +1
Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward design. However, existing methods…
cs.AI2025
Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
Jihwan Jeong, Xiaoyu Wang, Jingmin Wang +2
Offline reinforcement learning (RL) is crucial when online exploration is costly or unsafe but often struggles with high epistemic uncertainty due to limited data. Existing methods…