10 citations · 14 across the 5 of their papers we have counts for
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cs.MA2023
Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning
Xin Yu, Rongye Shi, Pu Feng +4
Incorporating symmetry as an inductive bias into multi-agent reinforcement learning (MARL) has led to improvements in generalization, data efficiency, and physical consistency. Whi…
cs.MA2023★ 10 cited
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning
Xin Yu, Rongye Shi, Pu Feng +3
Multi-agent reinforcement learning (MARL) has achieved promising results in recent years. However, most existing reinforcement learning methods require a large amount of data for m…
cs.LG2023★ 3 cited
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
Simin Li, Jun Guo, Jingqiao Xiu +8
This study probes the vulnerabilities of cooperative multi-agent reinforcement learning (c-MARL) under adversarial attacks, a critical determinant of c-MARL's worst-case performanc…