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News/Vulnerability Analysis for Safe Multi-Agent Reinforcement
arxiv
PublishedOctober 21, 2026 at 4:00 AM

Vulnerability Analysis for Safe Multi-Agent Reinforcement

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Publisher summary· verbatim

arXiv:2610.27293v1 Announce Type: new Abstract: Safe Multi-Agent Reinforcement Learning (SMARL) aims to learn cooperative control policies under prescribed safety constraints. Al- though existing SMARL methods focus on enforcing safety during policy optimization, their vulnerability remains largely

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