# Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2204.01597 · HTML: https://mlsanity.com/paper/2204.01597/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design](https://mlsanity.com/paper/1904.05242/) (2019)
- [REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association](https://mlsanity.com/paper/2010.01126/) (2020)
- [Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points](https://mlsanity.com/paper/2111.02258/) (2021)
- [Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space](https://mlsanity.com/paper/2011.09747/) (2020)
- [Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning](https://mlsanity.com/paper/2106.00845/) (2021)

## Built on by (most cited first, 4 of 4)

- [Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments](https://mlsanity.com/paper/2303.17655/) (2023)
- [Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning](https://mlsanity.com/paper/2212.12329/) (2022)
- [Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks](https://mlsanity.com/paper/2306.08785/) (2023)
- [Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction](https://mlsanity.com/paper/2512.20902/) (2025)

## Abstract (arXiv)

In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent reinforcement learning approaches optimise the system's EE using a 2D trajectory design, neglecting interference from nearby UAV cells. We aim to maximise the system's EE by jointly optimising each UAV's 3D trajectory, number of connected users, and the energy consumed, while accounting for interference. Thus, we propose a cooperative Multi-Agent Decentralised Double Deep Q-Network (MAD-DDQN) approach. Our approach outperforms existing baselines in terms of EE by as much as 55 -- 80%.