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Author

Guido Marchetto

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Open access 2026

Adaptive SDN Autoscaling via Generalizable Multi-Agent Reinforcement Learning With EAGLE

This work proposes EAGLE, a Multi-Agent Reinforcement Learning (MARL) system that autonomously orchestrates the scaling of network resources to meet flow demands and reduce power consumption, and shows that the trained model can “zero-shot generalize” to unseen network topologies that share structural or statistical similarity with the training domain, hence reducing training time and associated energy costs.

Doriana Monaco, Alessio Sacco, Flavio Esposito et al. · 0 citations

Packets in P4 Switches With Multi-Agent Decisions Logic

ROAR is proposed, a novel architectural solution that implements Deep Reinforcement Learning (DRL) inside P4 programmable switches to perform adaptive routing policies based on network conditions and traffic patterns that show both a throughput and delay improvement in the transmission compared to traditional approaches.

Antonino Angi, Alessio Sacco, Flavio Esposito et al. · 0 citations