Corpus ID: 211021045

Preventing Imitation Learning with Adversarial Policy Ensembles

@article{Zhan2020PreventingIL,
  title={Preventing Imitation Learning with Adversarial Policy Ensembles},
  author={Albert Zhan and Stas Tiomkin and P. Abbeel},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.01059}
}
Imitation learning can reproduce policies by observing experts, which poses a problem regarding policy privacy. Policies, such as human, or policies on deployed robots, can all be cloned without consent from the owners. How can we protect against external observers cloning our proprietary policies? To answer this question we introduce a new reinforcement learning framework, where we train an ensemble of near-optimal policies, whose demonstrations are guaranteed to be useless for an external… Expand
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