no code implementations • 31 Mar 2023 • Guillermo Bernárdez, José Suárez-Varela, Albert López, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization.
1 code implementation • 3 Sep 2021 • Guillermo Bernárdez, José Suárez-Varela, Albert López, Bo Wu, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.
BIG-bench Machine Learning Multi-agent Reinforcement Learning +1
1 code implementation • 26 Jul 2021 • José Suárez-Varela, Miquel Ferriol-Galmés, Albert López, Paul Almasan, Guillermo Bernárdez, David Pujol-Perich, Krzysztof Rusek, Loïck Bonniot, Christoph Neumann, François Schnitzler, François Taïani, Martin Happ, Christian Maier, Jia Lei Du, Matthias Herlich, Peter Dorfinger, Nick Vincent Hainke, Stefan Venz, Johannes Wegener, Henrike Wissing, Bo Wu, Shihan Xiao, Pere Barlet-Ros, Albert Cabellos-Aparicio
During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments.