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🔗 Source: proceedings.mlr.press
📊 Size: 817 KB
👤 Author: Anonymous Submission
⬇️ Downloads: 62

Summary

Adversarially Robust Imitation Learning (ARIL) is proposed to address the vulnerability of deep neural networks (DNNs) to subtle noise in imitation learning. An agent and an adversary are trained alternately, with the agent mimicking the behavior of an online expert under adversarially attacked input and the adversary learning to add perturbations to the states. The method is theoretically proven to achieve adversarial robustness and is evaluated on multiple benchmarks from DM Control Suite, showing improved robustness under both sensory and physical attacks. The paper contributes to the theory and practice of ARIL, defines sensory and physical attacks, and proves the method's effectiveness in achieving adversarial robustness.

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Description

Adversarially Robust Imitation Learning (ARIL) is proposed to address the vulnerability of deep neural networks (DNNs) to subtle noise in imitation learning.

Technical Information

  • File Format: PDF
  • File Size: 817 KB
  • Pages: 12
  • Language: EN
  • Author: Anonymous Submission
  • Total Downloads: 62
  • Last Updated: 7 days ago

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