- Working Paper
In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training. We propose a system called Robust StudentDQN (RS-DQN), which permits online robustness training alongside Q networks, while preserving competitive performance. We show that RS-DQN can be combined with (i) state-of-the-art adversarial training and (ii) provably robust training to obtain an agent that is resilient to strong attacks during training and evaluation. Show more
Journal / seriesarXiv
Pages / Article No.
Organisational unit03948 - Vechev, Martin / Vechev, Martin
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