Adversarial Policies: Attacking Deep Reinforcement Learning · HackerPapers discussions

Adversarial Policies: Attacking Deep Reinforcement Learning — mentioned by colsmit

Sat, 29 Feb 2020 21:24:53 GMT

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 behavior.If a driverless car misclassifies input from its camera, it could fall back on other sensors, for example.” TIL fail-safe components are 1) ubiquitous 2) work 3) only an option for supervised learning components. “A supervised learning model, trained to classify images, say, is tested on a different data set from…

Paper and discussion history. A mention is not an endorsement.

Adversarial Policies: Attacking Deep Reinforcement Learning — mentioned by tastroder

Sat, 29 Feb 2020 21:05:21 GMT

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 You have to stretch the definition of "new" somewhat to come up with the title TR chose, adversarial effects in all kinds of learning settings certainly aren't, the paper itself seems to contain quite interesting thoughts on how to assess them though (as opposed to just using them to steer the training process).

Paper and discussion history. A mention is not an endorsement.

Adversarial Policies: Attacking Deep Reinforcement Learning — HN submission

Fri, 28 Feb 2020 23:34:49 GMT

Adversarial Policies: Attacking Deep Reinforcement Learning

75 points; 7 discussion replies on HN. Paper and source history. A submission is not an endorsement.