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Jun, 2023
多智能体强化学习的鲁棒性测试:对关键智能体进行状态扰动
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents
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Ziyuan Zhou, Guanjun Liu
TL;DR
提出了一种新颖的基于DE的关键代理的Robustness Testing框架,用于生成关键代理的对抗性状态扰动,是第一个具有不同受害者代理的鲁棒性测试框架,表现出对受害者代理数量和破坏合作策略方面的卓越性能。
Abstract
multi-agent reinforcement learning
(MARL) has been widely applied in many fields such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to
adversarial perturbations
on ag
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