Zhenxing Gao

Explainable Reinforcement Learning Framework for Autonomous Windshear Escape with Policy Distillation

This study proposes an explainable, data-driven framework integrating active-reward proximal policy optimization (AR-PPO), which successfully distills black-box AI strategies into verifiable, physics-informed standard operating procedures (SOPs), providing a highly transparent and robust solution for autonomous windshear escape and future competency-based flight training.