Reinforcement-learning anomaly detection that ignores malicious data is positioned to filter spoofed fiducials and dazzling-induced sensor outputs.

Reinforcement-learning agents detecting anomalous events and ignoring malicious data are an explicit control against fabricated sensor measurements.