# Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks

arXiv: https://arxiv.org/abs/2306.08785 · HTML: https://mlsanity.com/paper/2306.08785/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points](https://mlsanity.com/paper/2111.02258/) (2021)
- [Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning](https://mlsanity.com/paper/2204.01597/) (2022)
- [Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning](https://mlsanity.com/paper/2106.00845/) (2021)
- [Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks](https://mlsanity.com/paper/2207.06131/) (2022)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles. However, the density of vehicles on roads often varies spatially and temporally primarily due to mobility and traffic situations in a geographical area, making it difficult to provide ubiquitous service. Moreover, as energy-constrained UAVs hover in the sky while serving mobile users, they may be faced with interference from nearby UAV cells or other access points sharing the same frequency band, thereby impacting the system's energy efficiency (EE). Recent multi-agent reinforcement learning (MARL) approaches applied to optimise the users' coverage worked well in reasonably even densities but might not perform as well in uneven users' distribution, i.e., in urban road networks with uneven concentration of vehicles. In this work, we propose a density-aware communication-enabled multi-agent decentralised double deep Q-network (DACEMAD-DDQN) approach that maximises the total system's EE by jointly optimising the trajectory of each UAV, the number of connected users, and the UAVs' energy consumption while keeping track of dense and uneven users' distribution. Our result outperforms state-of-the-art MARL approaches in terms of EE by as much as 65% - 85%.