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News/Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback
arxiv
PublishedApril 10, 2026 at 4:00 AM
—neutral

Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback

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Publisher summary· verbatim

arXiv:2603.28281v2 Announce Type: replace Abstract: We consider robustness against data corruption in offline multi-agent reinforcement learning from human feedback (MARLHF) under a strong-contamination model: given a dataset $D$ of trajectory-preference tuples (each preference being an $n$-dimensio

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arxiv
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#machine-learning#reinforcement-learning#robustness#adversarial-attacks
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