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Enhanced Adversarial Strategically-Timed Attacks Against Deep Reinforcement Learning

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IEEE ICASSP 2020 Virtual Conference May 2020

Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., aut

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Enhanced Adversarial Strategically-Timed Attacks Against Deep Reinforcement Learning

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Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., aut