学术文献库

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains, particularly in wireless communications. The future sixth-generation (6G) networks are expected to provide scalable, low-latency, ultra-reliable services empowered by the application of data-driven Artificial Intelligence (AI). The key enabling technologies of future 6G networks, such as intelligent meta-surfaces, aerial networks, and AI at the edge, involve more than one agent which motivates the importance of multi-agent learning techniques. Furthermore, cooperation is central to establishing self-organizing, self-sustaining, and decentralized networks. In this context, this tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled 6G networks. The first part of this paper will present a clear overview of the mathematical frameworks for single-agent RL and MARL. The main idea of this work is to motivate the application of RL beyond the model-free perspective which was extensively adopted in recent years. Thus, we provide a selective description of RL algorithms such as Model-Based RL (MBRL) and cooperative MARL and we highlight their potential applications in 6G wireless networks. Finally, we overview the state-of-the-art of MARL in fields such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAV) networks, and cell-free massive MIMO, and identify promising future research directions. We expect this tutorial to stimulate more research endeavors to build scalable and decentralized systems based on MARL.