A curriculum that aligns temporal-difference error distributions across increasing adversarial perturbations is claimed to make UAV policies robust to unseen GNSS spoofing attacks, with a generalization certificate.
Distributionally robust model-based offline reinforcement learning with near-optimal sample com- plexity,
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Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
A curriculum that aligns temporal-difference error distributions across increasing adversarial perturbations is claimed to make UAV policies robust to unseen GNSS spoofing attacks, with a generalization certificate.