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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.LG2025
Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics
Chenyang Cao, Miguel Rogel-García, Mohamed Nabail +2
Preference-based Reinforcement Learning (PbRL) provides a promising alternative to heuristic reward design in complex robotic environments. However, PbRL often suffers from poor sa…