Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. (arXiv:2201.00762v1 [cs.LG])

Data poisoning for reinforcement learning has historically focused on general
performance degradation, and targeted attacks have been successful via
perturbations that involve control of the victim's policy and rewards. We
introduce an insidious poisoning attack for reinforcement learning which causes
agent misbehavior only at specific target states - all while minimally
modifying a small fraction of training observations without assuming any
control over policy or reward. We accomplish this by adapting a recent
technique, gradient alignment, to reinforcement learning. We …

data order poisoning