新框架验证了学习到的多智能体通信策略的安全性

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Ahmad Farooq, Kamran Iqbal · 2026-06-19 04:00

   Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation

   arXiv:2606.19632v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in d…

2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Kamran Iqbal · 2026-06-17 22:22

   通过决策树蒸馏对学习到的多智能体通信策略进行形式化验证

   Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets. We pres…

3. Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-17 22:22

   Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation

   Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets. We pres…

4. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Almond Kiruthu Murimi · 2026-06-15 13:52

   BARD-MARL：多智能体强化学习中学习通信的拜占庭智能体检测

   Learned communication improves coordination in cooperative multi-agent reinforcement learning, but it also creates a trust problem: a trained policy may route information through agents that have become faulty or adversarial. This paper studies Byzantine-agent detection for learn…