The actual paper seems to be this: https://arxiv.org/abs/1905.10615 https://arxiv.org/abs/1905.10615 PDF: https://arxiv.org/pdf/1905.10615.pdf https://arxiv.or

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