Adversarial robustness of deep reinforcement learning
Over the past decades, the advancements in deep reinforcement learning (DRL) have demonstrated that deep neural network (DNN) policies can be trained to prescribe near-optimal actions in many complex tasks. Unfortunately, DNN policies are shown to be vulnerable to adversarial perturbations in the in...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/154587 |