Deep Reinforcement Learning for Communication Networks

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.

Authors

• Raffaele Galliera University of West Florida & IHMC

Keywords

• Artificial Intelligence
• Communication Networks
• Deep Reinforcement Learning
• Distributed Systems
• Emergent Communication
• Learning To Communicate
• machine learning
• Multi-Agent Reinforcement Learning
• Multi-agent Systems