Multi-Agent Deep Reinforcement Learning for UAV-Assisted 5G Network Slicing: A Comparative Study of MAPPO, MADDPG, and MADQN

TL;DR

This paper compares three multi-agent deep reinforcement learning algorithms—MAPPO, MADDPG, and MADQN—for optimizing UAV deployment and resource management in 5G network slicing, demonstrating their strengths in different scenarios.

Contribution

It introduces a MADRL framework integrating multiple algorithms for UAV-assisted 5G slicing, providing a comprehensive performance comparison in urban and rural environments.

Findings

MAPPO achieves the best QoS-energy tradeoff in interference-rich scenarios.

MADDPG provides precise control and higher SINR in rural settings.

MADQN offers computational efficiency as a baseline.

Abstract

The growing demand for robust, scalable wireless networks in the 5G-and-beyond era has led to the deployment of Unmanned Aerial Vehicles (UAVs) as mobile base stations to enhance coverage in dense urban and underserved rural areas. This paper presents a Multi-Agent Deep Reinforcement Learning (MADRL) framework that integrates Proximal Policy Optimization (MAPPO), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and Multi-Agent Deep Q-Networks (MADQN) to jointly optimize UAV positioning, resource allocation, Quality of Service (QoS), and energy efficiency through 5G network slicing. The framework adopts Centralized Training with Decentralized Execution (CTDE), enabling autonomous real-time decision-making while preserving global coordination. Users are prioritized into Premium (A), Silver (B), and Bronze (C) slices with distinct QoS requirements. Experiments in realistic urban and rural scenarios show that MAPPO achieves the best overall QoS-energy tradeoff, especially in interference-rich environments; MADDPG offers more precise continuous control and can attain slightly higher SINR in open rural settings at the cost of increased energy usage; and MADQN provides a computationally efficient baseline for discretized action spaces. These findings demonstrate that no single MARL algorithm is universally dominant; instead, algorithm suitability depends on environmental topology, user density, and service requirements. The proposed framework highlights the potential of MARL-driven UAV systems to enhance scalability, reliability, and differentiated QoS delivery in next-generation wireless networks.