# PARRoT: Predictive Ad-hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge

arXiv: https://arxiv.org/abs/2012.05490 · HTML: https://mlsanity.com/paper/2012.05490/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Fully-echoed Q-routing with Simulated Annealing Inference for Flying Adhoc Networks](https://mlsanity.com/paper/2103.12870/) (2021)
- [Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency](https://mlsanity.com/paper/2207.11386/) (2022)
- [Continual Learning to Generalize Forwarding Strategies for Diverse Mobile Wireless Networks](https://mlsanity.com/paper/2509.23913/) (2025)

## Abstract (arXiv)

Swarms of collaborating Unmanned Aerial Vehicles (UAVs) that utilize ad-hoc networking technologies for coordinating their actions offer the potential to catalyze emerging research fields such as autonomous exploration of disaster areas, demanddriven network provisioning, and near field packet delivery in Intelligent Transportation Systems (ITSs). As these mobile robotic networks are characterized by high grades of relative mobility, existing routing protocols often fail to adopt their decision making to the implied network topology dynamics. For addressing these challenges, we present Predictive Ad-hoc Routing fueled by Reinforcement learning and Trajectory knowledge (PARRoT) as a novel machine learning-enabled routing protocol which exploits mobility control information for integrating knowledge about the future motion of the mobile agents into the routing process. The performance of the proposed routing approach is evaluated using comprehensive network simulation. In comparison to established routing protocols, PARRoT achieves a massively higher robustness and a significantly lower end-to-end latency.