The paper gives the first tight necessity and sufficiency conditions for successful reward poisoning attacks in linear MDPs.
Reward poisoning in reinforcement learning: Attacks against un- known learners in unknown environments.arXiv preprint arXiv:2102.08492,
4 Pith papers cite this work. Polarity classification is still indexing.
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The IBAL framework builds information-theoretic attacks that break agent interactions in MARL and trains policies to stay robust under observation and action perturbations.
Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.
citing papers explorer
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When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPs
The paper gives the first tight necessity and sufficiency conditions for successful reward poisoning attacks in linear MDPs.
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Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
The IBAL framework builds information-theoretic attacks that break agent interactions in MARL and trains policies to stay robust under observation and action perturbations.
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Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.
- Efficient Preference Poisoning Attack on Offline RLHF