Learning multi-agent coordination through connectivity-driven communication

DOI10.1007/s10994-022-06286-6zbMath1525.68136arXiv2002.05233MaRDI QIDQ6155197

Emanuele Pesce, Giovanni Montana

Publication date: 12 June 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.05233

zbMATH Keywords

neural networksgraphsmulti-agent systemreinforcement learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Agent technology and artificial intelligence (68T42)

Cites Work

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• Graph spectral image smoothing using the heat kernel
• Synchronization in networks of identical linear systems
• Autonomous agents modelling other agents: a comprehensive survey and open problems
• Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication
• Heritage and early history of the boundary element method
• Consensus in multi-agent systems with communication constraints
• A New Scaling and Squaring Algorithm for the Matrix Exponential
• Factor graphs and the sum-product algorithm
• Multi-agent systems applications in manufacturing systems and supply chain management: a review paper

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