# Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.06215 · HTML: https://mlsanity.com/paper/2002.06215/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 30 of 42)

- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Learning Resilient Radio Resource Management Policies with Graph Neural Networks](https://mlsanity.com/paper/2203.11012/) (2022)
- [State-Augmented Learnable Algorithms for Resource Management in Wireless Networks](https://mlsanity.com/paper/2207.02242/) (2022)
- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets](https://mlsanity.com/paper/2311.11423/) (2023)
- [Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.08421/) (2024)
- [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024)
- [Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2312.00279/) (2023)
- [Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10968/) (2022)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [Advancing RAN Slicing with Offline Reinforcement Learning](https://mlsanity.com/paper/2312.10547/) (2023)
- [An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management](https://mlsanity.com/paper/2501.12991/) (2025)
- [Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels](https://mlsanity.com/paper/2110.01722/) (2021)
- [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)
- [Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.16606/) (2025)
- [A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing](https://mlsanity.com/paper/2110.02736/) (2021)
- [Offline and Distributional Reinforcement Learning for Radio Resource Management](https://mlsanity.com/paper/2409.16764/) (2024)
- [Average Reward Reinforcement Learning for Wireless Radio Resource Management](https://mlsanity.com/paper/2501.06700/) (2025)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression](https://mlsanity.com/paper/2506.18748/) (2025)
- [Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.14355/) (2023)
- [Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Model](https://mlsanity.com/paper/2407.19765/) (2024)
- [Deep Index Policy for Multi-Resource Restless Matching Bandit and Its Application in Multi-Channel Scheduling](https://mlsanity.com/paper/2408.07205/) (2024)
- [Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing](https://mlsanity.com/paper/2506.00574/) (2025)
- [A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks](https://mlsanity.com/paper/2110.08704/) (2021)
- [Kindness in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2311.04239/) (2023)
- [Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks](https://mlsanity.com/paper/2404.03227/) (2024)
- [Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum](https://mlsanity.com/paper/2501.15802/) (2025)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)

## Abstract (arXiv)

We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL agent that receives delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval. Our proposed framework enables agents to make decisions simultaneously and in a distributed manner, unaware of the concurrent decisions of other agents. Moreover, our design of the agents' observation and action spaces is scalable, in the sense that an agent trained on a scenario with a specific number of transmitters and users can be applied to scenarios with different numbers of transmitters and/or users. Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5th percentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized information-theoretic baseline. We also show that our trained agents are robust and maintain their performance gains when experiencing mismatches between train and test deployments.