Corpus ID: 26914589

Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

@article{Behzadan2017WhateverDN,
  title={Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger},
  author={Vahid Behzadan and Arslan Munir},
  journal={ArXiv},
  year={2017},
  volume={abs/1712.09344}
}
Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations. In this paper, we investigate the robustness and resilience of deep RL to training-time and test-time attacks. Through experimental results, we demonstrate that under noncontiguous training-time attacks, Deep Q-Network (DQN) agents can recover and adapt to the adversarial conditions by reactively adjusting the policy. Our results also show that… Expand
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