ASGARD: Action-Space Guard for UAV Resilience via Reinforcement Learning

ASGARD is a two‑phase teacher‑student framework that protects reinforcement‑learning controllers for UAVs from action‑space attacks. In the teacher phase, an encoder fuses the UAV’s physical state with privileged attack information to generate an action‑attack‑aware latent representation, which trains both the control policy and a monitor that corrects actions before they reach the actuators. The student phase then learns to replicate the encoder and monitor using only the UAV’s physical state history, enabling on‑board resilience. Experiments show that ASGARD remains effective against various attack scenarios, including unseen and stealthy attacks, allowing UAV missions to complete successfully.

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• reinforcement-learning