Shi-Xiong Jiang

Vulnerability Analysis for Safe Multi-Agent Reinforcement Learning

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 unexplored. This paper investigates inference-time observation attac...