43 citations · 53 across the 4 of their papers we have counts for
5 papers · 1 filter
Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning
Wei Duan, Jie Lu, En Yu +1
Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and communication links as edges. While r…
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Wei Duan, Jie Lu, Junyu Xuan
Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for lear…
Layer-diverse Negative Sampling for Graph Neural Networks
Wei Duan, Jie Lu, Yu Guang Wang +1
Graph neural networks (GNNs) are a powerful solution for various structure learning applications due to their strong representation capabilities for graph data. However, traditiona…
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
Wei Duan, Jie Lu, Junyu Xuan
Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph l…
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
Wei Duan, Junyu Xuan, Maoying Qiao +1
Graph Convolutional Neural Networks (GCNs) has been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think…