10 citations · 16 across the 8 of their papers we have counts for
8 papers
Symmetry-Guided Multi-Agent Inverse Reinforcement Learning
Yongkai Tian, Yirong Qi, Xin Yu +2
In robotic systems, the performance of reinforcement learning depends on the rationality of predefined reward functions. However, manually designed reward functions often lead to p…
Embedded Mean Field Reinforcement Learning for Perimeter-defense Game
Li Wang, Xin Yu, Xuxin Lv +2
With the rapid advancement of unmanned aerial vehicles (UAVs) and missile technologies, perimeter-defense game between attackers and defenders for the protection of critical region…
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
Pu Feng, Junkang Liang, Size Wang +6
In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in…
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…