# Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.14297 · HTML: https://mlsanity.com/paper/2007.14297/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility](https://mlsanity.com/paper/2203.07676/) (2022)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems](https://mlsanity.com/paper/2104.01527/) (2021)
- [Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning](https://mlsanity.com/paper/2105.04207/) (2021)
- [Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey](https://mlsanity.com/paper/2103.10134/) (2021)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing](https://mlsanity.com/paper/2312.10418/) (2023)
- [AoI-minimizing Scheduling in UAV-relayed IoT Networks](https://mlsanity.com/paper/2107.05181/) (2021)
- [Towards Fault Tolerance in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.00534/) (2024)
- [A Practical AoI Scheduler in IoT Networks with Relays](https://mlsanity.com/paper/2203.04227/) (2022)
- [VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT](https://mlsanity.com/paper/2210.08132/) (2022)
- [Adaptive Control of Resource Flow to Optimize Construction Work and Cash Flow via Online Deep Reinforcement Learning](https://mlsanity.com/paper/2307.10574/) (2023)
- [Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning](https://mlsanity.com/paper/2308.10124/) (2023)
- [A Reinforcement Learning Framework for Resource Allocation in Uplink Carrier Aggregation in the Presence of Self Interference](https://mlsanity.com/paper/2511.17931/) (2025)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)
- [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)

Due to the advantages of flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) have great potential for sensing applications in the next generation of cellular networks, which will give rise to a cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the UAVs execute sensing tasks through cooperative sensing and transmission to minimize the age of information (AoI). However, the cooperative sensing and transmission is tightly coupled with the UAVs' trajectories, which makes the trajectory design challenging. To tackle this challenge, we propose a distributed sense-and-send protocol, where the UAVs determine the trajectories by selecting from a discrete set of tasks and a continuous set of locations for sensing and transmission. Based on this protocol, we formulate the trajectory design problem for AoI minimization and propose a compound-action actor-critic (CA2C) algorithm to solve it based on deep reinforcement learning. The CA2C algorithm can learn the optimal policies for actions involving both continuous and discrete variables and is suited for the trajectory design. {Our simulation results show that the CA2C algorithm outperforms four baseline algorithms}. Also, we show that by dividing the tasks, cooperative UAVs can achieve a lower AoI compared to non-cooperative UAVs.