Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

By Amal Feriani, Ekram Hossain. IEEE Communications Surveys and Tutorials, 2020

359 citations recorded by the source corpus.

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 next generation of wireless networks is 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 wireless 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 wireless 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 future 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.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6b5e8a917cebbacc4937f6d033b12a7a5de683ca

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

  1. Yuanwei Liu
  2. Rui Zhang
  3. Walid Saad
  4. Medhi Bennis
  5. Xidong Mu
  6. Robert Schober
  7. Ekram Hossain
  8. Naofal Al-Dhahir

In Electrical and Electronic Engineering

  1. H. Vincent Poor
  2. Yuanwei Liu
  3. Robert Schober
  4. Rui Zhang
  5. Zhiguo Ding
  6. Medhi Bennis
  7. Xidong Mu
  8. Walid Saad

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