10 citations · 16 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
Tianhao Peng, Wenjun Wu, Haitao Yuan +6
Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on hetero…
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…
Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
Simin Li, Jun Guo, Jingqiao Xiu +6
In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case acti…
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…