# Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocol

arXiv: https://arxiv.org/abs/1809.02934 · HTML: https://mlsanity.com/paper/1809.02934/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs](https://mlsanity.com/paper/2402.02957/) (2024)
- [Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks](https://mlsanity.com/paper/2410.22578/) (2024)
- [Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle](https://mlsanity.com/paper/2202.08972/) (2022)
- [Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment](https://mlsanity.com/paper/2603.16141/) (2026)
- [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)

Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks, which sense the conditions of the tasks and transmit the real-time sensory data to the base station (BS). The performance of a UAV is determined by the performance of both its sensing and transmission processes, which are influenced by the trajectory of the UAV. However, it is challenging for UAVs to design their trajectories efficiently, since they work in a dynamic environment. To tackle this challenge, in this paper, we adopt the reinforcement learning framework to solve the UAV trajectory design problem in a decentralized manner. To coordinate multiple UAVs performing the real-time sensing tasks, we first propose a sense-and-send protocol, and analyze the probability for successful valid data transmission using nested Markov chains. Then, we formulate the decentralized trajectory design problem and propose an enhanced multi-UAV Q-learning algorithm to solve this problem. Simulation results show that the proposed enhanced multi-UAV Q-learning algorithm converges faster and achieves higher utilities for the UAVs in the real-time task-sensing scenarios.