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• W3132858839 abstract "We study black-box reward poisoning attacks against reinforcement learning (RL), in which an adversary aims to manipulate the rewards to mislead a sequence of RL agents with unknown algorithms to learn a nefarious policy in an environment unknown to the adversary a priori. That is, our attack makes minimum assumptions on the prior knowledge of the adversary: it has no initial knowledge of the environment or the learner, and neither does it observe the learner's internal mechanism except for its performed actions. We design a novel black-box attack, U2, that can provably achieve a near-matching performance to the state-of-the-art white-box attack, demonstrating the feasibility of reward poisoning even in the most challenging black-box setting." @default.
• W3132858839 created "2021-03-01" @default.
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• W3132858839 date "2021-02-16" @default.
• W3132858839 modified "2023-09-24" @default.
• W3132858839 title "Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments." @default.
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• W3132858839 hasPublicationYear "2021" @default.
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