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

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 by the agents. Existing attacks are feasible in principle, but face challenges in practice, either by being too slow to...

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Hauptverfasser:	Tekgul, Buse G. A, Wang, Shelly, Marchal, Samuel, Asokan, N
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Cryptography and Security Computer Science - Learning
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