HackerPapers

arXiv:1905.10615cs.LG

Adversarial Policies: Attacking Deep Reinforcement Learning

Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell

Abstract

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations. This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial? We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents. The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior. We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent. Videos are available at https://adversarialpolicies.github.io/.

arXiv comments
Presented at ICLR 2020
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence cs.CRCryptography and Security stat.MLMachine Learning (Statistics)

On Hacker News

Linked by 2 identifiable commenters in 2 comments across 1 thread, from Feb 2020 to Feb 2020. Found in 1 HN submission from Feb 2020 to Feb 2020. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. Adversarial Policies: Attacking Deep Reinforcement Learning 2 comments

    • The actual paper seems to be this: https://arxiv.org/abs/1905.10615 PDF: https://arxiv.org/pdf/1905.10615.pdf Website with videos: https://adversarialpolicies.github.io/ (that would make a better submission imho) Github: https://github.com/HumanCompatibleAI/adversarial-policies…

      tastroderView on HNParent context

    • This article is not good, I encourage reading the paper its based on instead: https://arxiv.org/pdf/1905.10615.pdf “In some ways, adversarial policies are more worrying than attacks on supervised learning models, because reinforcement learning policies govern an AI’s overall…

      colsmitView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.