Calibrated Safe Policy Improvement for Continuous Offline Reinforcement Learning

Safe Policy Improvement (SPI) aims to improve a baseline policy offline using fixed data while avoiding performance degradation with high probability. Existing SPI methods work in discrete domains but do not extend to continuous control. We propose Calibrated Safe Policy Improvement (Cal-SPI), a Monte Carlo Tree Search-based continuous SPI method that replaces count-based support with calibrated model trust. Cal-SPI learns a deep ensemble dynamics model and calibrates ensemble disagreement on held-out data through a Wilks-style tolerance construction, turning raw uncertainty into a statistical certificate of one-step model error. This certificate defines a hard gate within model-based tree search. We provide a theoretical analysis showing that calibrated model trust and a baseline-relative switching criterion yield a conditional PAC-style improvement bound for gate-certified root decisions. Experiments across three MuJoCo continuous domains show that Cal-SPI avoids performance degradation in low-data regimes and improves over the baseline once calibrated model trust becomes reliable.