# Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

arXiv: https://arxiv.org/abs/2207.11386 · HTML: https://mlsanity.com/paper/2207.11386/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 102 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [Experience-driven Networking: A Deep Reinforcement Learning based Approach](https://mlsanity.com/paper/1801.05757/) (2018)
- [CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing](https://mlsanity.com/paper/2111.08397/) (2021)
- [DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks](https://mlsanity.com/paper/2111.15013/) (2021)
- [Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs](https://mlsanity.com/paper/2101.03273/) (2021)
- [Relational Deep Reinforcement Learning for Routing in Wireless Networks](https://mlsanity.com/paper/2012.15700/) (2020)
- [PARRoT: Predictive Ad-hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge](https://mlsanity.com/paper/2012.05490/) (2020)
- [Fully-echoed Q-routing with Simulated Annealing Inference for Flying Adhoc Networks](https://mlsanity.com/paper/2103.12870/) (2021)
- [Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/1905.03494/) (2019)
- [Towards Machine Learning-Enabled Context Adaption for Reliable Aerial Mesh Routing](https://mlsanity.com/paper/2107.06190/) (2021)

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

- [Knowledge-Guided Machine Learning for Stabilizing Near-Shortest Path Routing](https://mlsanity.com/paper/2509.06640/) (2025)
- [Learning from A Single Graph is All You Need for Near-Shortest Path Routing in Wireless Networks](https://mlsanity.com/paper/2308.09829/) (2023)
- [Continual Learning to Generalize Forwarding Strategies for Diverse Mobile Wireless Networks](https://mlsanity.com/paper/2509.23913/) (2025)

## Abstract (arXiv)

Designing effective routing strategies for mobile wireless networks is challenging due to the need to seamlessly adapt routing behavior to spatially diverse and temporally changing network conditions. In this work, we use deep reinforcement learning (DeepRL) to learn a scalable and generalizable single-copy routing strategy for such networks. We make the following contributions: i) we design a reward function that enables the DeepRL agent to explicitly trade-off competing network goals, such as minimizing delay vs. the number of transmissions per packet; ii) we propose a novel set of relational neighborhood, path, and context features to characterize mobile wireless networks and model device mobility independently of a specific network topology; and iii) we use a flexible training approach that allows us to combine data from all packets and devices into a single offline centralized training set to train a single DeepRL agent. To evaluate generalizeability and scalability, we train our DeepRL agent on one mobile network scenario and then test it on other mobile scenarios, varying the number of devices and transmission ranges. Our results show our learned single-copy routing strategy outperforms all other strategies in terms of delay except for the optimal strategy, even on scenarios on which the DeepRL agent was not trained.