Online Robustness Training for Deep Reinforcement Learning
Autor: | Fischer, Marc, Mirman, Matthew, Stalder, Steven, Vechev, Martin |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: | |
Popis: | In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training. We propose a system called Robust Student-DQN (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. |
Databáze: | OpenAIRE |
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