  We propose the first black-box targeted attack against online deep
reinforcement learning through reward poisoning during training time. Our
attack is applicable to general environments with unknown dynamics learned by
unknown algorithms and requires limited attack budgets and computational
resources. We leverage a general framework and find conditions to ensure
efficient attack under a general assumption of the learning algorithms. We show
that our attack is optimal in our framework under the conditions. We
experimentally verify that with limited budgets, our attack efficiently leads
the learning agent to various target policies under a diverse set of popular
DRL environments and state-of-the-art learners.
