Selective real‐time adversarial perturbations against deep reinforcement learning agents
Abstract Recent work has shown that deep reinforcement learning (DRL) is vulnerable to adversarial attacks, so that exploiting vulnerabilities in DRL systems through adversarial attack techniques has become a necessary prerequisite for building robust DRL systems. Compared to traditional deep learni...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-03-01
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| Series: | IET Cyber-Physical Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cps2.12065 |