# Multi-Agent Reinforcement Learning for Multi-Cell Spectrum and Power Allocation

arXiv: https://arxiv.org/abs/2312.05746 · HTML: https://mlsanity.com/paper/2312.05746/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2023.

## Built on by (most cited first, 5 of 5)

- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints](https://mlsanity.com/paper/2608.01745/) (2026)
- [Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management](https://mlsanity.com/paper/2608.28878/) (2026)
- [ICMAPE: In-Context Multiagent Pure Exploration](https://mlsanity.com/paper/2609.33986/) (2026)

## Abstract (arXiv)

This paper introduces a novel approach to radio resource allocation in multi-cell wireless networks using a fully scalable multi-agent reinforcement learning (MARL) framework. A distributed method is developed where agents control individual cells and determine spectrum and power allocation based on limited local information, yet achieve quality of service (QoS) performance comparable to centralized methods using global information. The objective is to minimize packet delays across devices under stochastic arrivals and applies to both conflict graph abstractions and cellular network configurations. This is formulated as a distributed learning problem, implementing a multi-agent proximal policy optimization (MAPPO) algorithm with recurrent neural networks and queueing dynamics. This traffic-driven MARL-based solution enables decentralized training and execution, ensuring scalability to large networks. Extensive simulations demonstrate that the proposed methods achieve comparable QoS performance to genie-aided centralized algorithms with significantly less execution time. The trained policies also exhibit scalability and robustness across various network sizes and traffic conditions.