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

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2023

FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks

Buse G. A. Tekgul, N. Asokan

We propose FLARE, the first fingerprinting mechanism to verify whether a suspected Deep Reinforcement Learning (DRL) policy is an illegitimate copy of another (victim) policy. We f…

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…