Distilling Deep Reinforcement Learning into Interpretable Fuzzy Rules: An Explainable AI Framework

ArXi:2603.13257v1 Announce Type: new Deep Reinforcement Learning (DRL) agents achieve remarkable performance in continuous control but remain opaque, hindering deployment in safety-critical domains. Existing explainability methods either provide only local insights (SHAP, LIME) or employ over-simplified surrogates failing to capture continuous dynamics (decision trees