UBP2: Uncertainty-Balanced Preference Planning for Efficient Preference-based Reinforcement Learning

Preference-based RL provides an approach to learning reward models from pairwise comparisons between behaviors, bypassing the need for explicit reward design. However, existing methods typically rely on passive data collection and suffer from poor sample efficiency, especially during the early stages of learning. We introduce a model-based approach that actively directs exploration by jointly reasoning over uncertainties in the reward, dynamics and value functions. Our method, Uncertainty-Balanced Preference Planning (UBP2), uses ensembles of reward, dynamics and value function models to evaluate candidate trajectories according to a unified score combining expected reward, terminal value and epistemic uncertainty. Planning under this objective yields an explicit tradeoff between exploitation and information acquisition without requiring ad hoc exploration heuristics. Empirically, experiments on the Meta-World benchmarks show that UBP2 achieves substantially higher sample efficiency than model-free preference-based methods and non-optimistic model-based baselines.