43 citations · 59 across the 7 of their papers we have counts for
7 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…
Functional Stochastic Gradient MCMC for Bayesian Neural Networks
Mengjing Wu, Junyu Xuan, Jie Lu
Classical parameter-space Bayesian inference for Bayesian neural networks (BNNs) suffers from several unresolved prior issues, such as knowledge encoding intractability and patholo…
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…
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Wei Duan, Junyu Xuan, Maoying Qiao +1
Graph convolutional networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs general…