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.
However, such trial -and- error exploration is highly inefficient, especially under strict attack budgets and sparse rewards
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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.