# Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks

arXiv: https://arxiv.org/abs/2410.22578 · HTML: https://mlsanity.com/paper/2410.22578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12461/) (2020)
- [Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2003.04816/) (2020)
- [Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocol](https://mlsanity.com/paper/1809.02934/) (2018)
- [Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management](https://mlsanity.com/paper/1911.08771/) (2019)
- [Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery](https://mlsanity.com/paper/2112.00556/) (2021)
- [Trajectory Design in Rechargeable UAV Aided Wireless Networks: A Unified Framework for Energy Efficiency and Flight Time Minimization](https://mlsanity.com/paper/2104.13799/) (2021)

## Built on by (most cited first, 1 of 1)

- [Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward](https://mlsanity.com/paper/2605.24992/) (2026)

## Abstract (arXiv)

Mission-oriented drone networks have been widely used for structural inspection, disaster monitoring, border surveillance, etc. Due to the limited battery capacity of drones, mission execution strategy impacts network performance and mission completion. However, collaborative execution is a challenging problem for drones in such a dynamic environment as it also involves efficient trajectory design. We leverage multi-agent reinforcement learning (MARL) to manage the challenge in this study, letting each drone learn to collaboratively execute tasks and plan trajectories based on its current status and environment. Simulation results show that the proposed collaborative execution model can successfully complete the mission at least 80% of the time, regardless of task locations and lengths, and can even achieve a 100% success rate when the task density is not way too sparse. To the best of our knowledge, our work is one of the pioneer studies on leveraging MARL on collaborative execution for mission-oriented drone networks; the unique value of this work lies in drone battery level driving our model design.