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

arXiv: https://arxiv.org/abs/2011.03615 · HTML: https://mlsanity.com/paper/2011.03615/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 30 of 60)

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- [On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03491/) (2022)
- [Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks](https://mlsanity.com/paper/2410.07611/) (2024)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography](https://mlsanity.com/paper/2203.15413/) (2022)
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- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
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- [Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.09135/) (2025)
- [AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks](https://mlsanity.com/paper/2511.17506/) (2025)
- [When Do Multi-Agent Systems Outperform? Analysing the Learning Efficiency of Agentic Systems](https://mlsanity.com/paper/2602.08272/) (2026)
- [Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems](https://mlsanity.com/paper/2211.01107/) (2022)

## Abstract (arXiv)

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.