An expert-guided, annealed reinforcement learning adversary improves collision rates in most tested autonomous-driving scenarios, but the claim of consistent improvement is not supported by the paper's own data.
This design enables the expert to better capture diverse attack policies across various scenarios
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Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies
An expert-guided, annealed reinforcement learning adversary improves collision rates in most tested autonomous-driving scenarios, but the claim of consistent improvement is not supported by the paper's own data.