# Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2508.06863 · HTML: https://mlsanity.com/paper/2508.06863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing](https://mlsanity.com/paper/2009.11277/) (2020)
- [A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art](https://mlsanity.com/paper/1906.08452/) (2019)
- [Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/1911.03887/) (2019)
- [Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2007.15105/) (2020)
- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/2404.15292/) (2024)
- [Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects](https://mlsanity.com/paper/1711.08407/) (2017)
- [Mobile Edge Computing in Unmanned Aerial Vehicle Networks](https://mlsanity.com/paper/1910.10523/) (2019)
- [Graph Neural Network-based Multi-agent Reinforcement Learning for Resilient Distributed Coordination of Multi-Robot Systems](https://mlsanity.com/paper/2403.13093/) (2024)
- [Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring](https://mlsanity.com/paper/2011.01129/) (2020)
- [Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-based Multi-Agent Proximal Policy Optimization DRL with Attention Mechanism](https://mlsanity.com/paper/2209.07228/) (2022)
- [Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach](https://mlsanity.com/paper/2308.12756/) (2023)
- [User Association and Path Planning for UAV-Aided Mobile Edge Computing with Energy Restriction](https://mlsanity.com/paper/1910.10921/) (2019)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) have been recently utilized in multi-access edge computing (MEC) as edge servers. It is desirable to design UAVs' trajectories and user to UAV assignments to ensure satisfactory service to the users and energy efficient operation simultaneously. The posed optimization problem is challenging to solve because: (i) The formulated problem is non-convex, (ii) Due to the mobility of ground users, their future positions and channel gains are not known in advance, (iii) Local UAVs' observations should be communicated to a central entity that solves the optimization problem. The (semi-) centralized processing leads to communication overhead, communication/processing bottlenecks, lack of flexibility and scalability, and loss of robustness to system failures. To simultaneously address all these limitations, we advocate a fully decentralized setup with no centralized entity. Each UAV obtains its local observation and then communicates with its immediate neighbors only. After sharing information with neighbors, each UAV determines its next position via a locally run deep reinforcement learning (DRL) algorithm. None of the UAVs need to know the global communication graph. Two main components of our proposed solution are (i) Graph attention layers (GAT), and (ii) Experience and parameter sharing proximal policy optimization (EPS-PPO). Our proposed approach eliminates all the limitations of semi-centralized MADRL methods such as MAPPO and MA deep deterministic policy gradient (MADDPG), while guaranteeing a better performance than independent local DRLs such as in IPPO. Numerical results reveal notable performance gains in several different criteria compared to the existing MADDPG algorithm, demonstrating the potential for offering a better performance, while utilizing local communications only.