most citedReal-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses

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cs.LG2021★ 1 cited

Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses

Buse G. A. Tekgul, Shelly Wang, Samuel Marchal +1

Deep reinforcement learning (DRL) is vulnerable to adversarial perturbations. Adversaries can mislead the policies of DRL agents by perturbing the state of the environment observed…