Deep Reinforcement Learning for Communication Networks


  • Raffaele Galliera The University of West Florida The Institute for Human and Machine Cognition



Multi-Agent Reinforcement Learning, Deep Reinforcement Learning, Machine Learning, Artificial Intelligence, Multi-Agent Systems, Emergent Communication, Learning To Communicate, Communication Networks, Distributed Systems


This research explores optimizing communication tasks with (Multi-Agent) Reinforcement Learning (RL/MARL) in Point-to-Point and Group Communication (GC) networks. The study initially applied RL for Congestion Control in networks with dynamic link properties, yielding competitive results. Then, it focused on the challenge of effective message dissemination in GC networks, by framing a novel game-theoretic formulation and designing methods to solve the task based on MARL and Graph Convolution. Future research will deepen the exploration of MARL in GC. This will contribute to both academic knowledge and practical advancements in the next generation of communication protocols.




How to Cite

Galliera, R. (2024). Deep Reinforcement Learning for Communication Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23387-23388.