Meta-Uncertainty Calibration for Model-Based Offline RL

Build a meta-learner that ingests dataset-level and state-level diagnostics (nearest-neighbor distances, SUMO cross-entropy to data; DARL distances; ensemble variance; MC-dropout) and outputs calibrated uncertainty measures and thresholds used for reward penalties, rollout truncation, and exploration control. Train across tasks so the system learns when SUMO-like search-based uncertainty, DARL’s nonparametric distance, or ensemble/dropout hybrids are most predictive of true model error. For adaptively collected logs, include TMIS-style OPE error as a supervision signal for calibration. This is novel because prior work proposes individual uncertainty estimators but none adaptively select and calibrate among them conditioned on the dataset/task. This “portfolio of uncertainties” treats estimator choice as a learnable decision, not a fixed design. It extends observations about biased model exploration by providing calibrated, task-specific uncertainty that better governs rollout generation; integrates SUMO’s cross-entropy and DARL’s distance-awareness with ensemble/dropout signals; and leverages adaptive-OPE insights to close the loop on real error calibration. Better uncertainty means better synthetic data and safer policy improvement—particularly impactful for model-based offline RL pipelines. It should reduce over/under-penalization pathologies that plague performance. Impact is a general, plug-and-play uncertainty layer that can standardize and strengthen model-based offline RL across domains.

References:

1. Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data. Sunil Madhow, Dan Xiao, Ming Yin, Yu-Xiang Wang (2023). arXiv.org.
2. SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning. Zhongjian Qiao, Jiafei Lyu, Kechen Jiao, Qi Liu, Xiu Li (2024). AAAI Conference on Artificial Intelligence.
3. DARL: Distance-Aware Uncertainty Estimation for Offline Reinforcement Learning. Hongchang Zhang, Jianzhun Shao, Shuncheng He, Yuhang Jiang, Xiangyang Ji (2023). AAAI Conference on Artificial Intelligence.