Lan-Tao Liu

SEVI: Safe Evidential Real-World Reinforcement Learning

Safe Evidential Real-World Reinforcement Learning (SEVI), a framework for reliability-aware safety intervention during real-world policy learning, is proposed and an Evidential Safety Critic is introduced that estimates both future constraint-violation risk and epistemic uncertainty in a single forward pass.