# DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks

arXiv: https://arxiv.org/abs/2111.15013 · HTML: https://mlsanity.com/paper/2111.15013/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization](https://mlsanity.com/paper/1709.07080/) (2017)
- [Hierarchical Deep Double Q-Routing](https://mlsanity.com/paper/1910.04041/) (2019)

## Built on by (most cited first, 6 of 6)

- [Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency](https://mlsanity.com/paper/2207.11386/) (2022)
- [DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09637/) (2023)
- [DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing](https://mlsanity.com/paper/2302.13877/) (2023)
- [Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks](https://mlsanity.com/paper/2509.04973/) (2025)
- [Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.16198/) (2023)
- [Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment](https://mlsanity.com/paper/2603.16141/) (2026)

## Abstract (arXiv)

Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this paper, we present DeepCQ+ routing protocol which, in a novel manner integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into existing Q-learning-based routing protocols and their variants and achieves persistently higher performance across a wide range of topology and mobility configurations. While keeping the overall protocol structure of the Q-learning-based routing protocols, DeepCQ+ replaces statically configured parameterized thresholds and hand-written rules with carefully designed MADRL agents such that no configuration of such parameters is required a priori. Extensive simulation shows that DeepCQ+ yields significantly increased end-to-end throughput with lower overhead and no apparent degradation of end-to-end delays (hop counts) compared to its Q-learning based counterparts. Qualitatively, and perhaps more significantly, DeepCQ+ maintains remarkably similar performance gains under many scenarios that it was not trained for in terms of network sizes, mobility conditions, and traffic dynamics. To the best of our knowledge, this is the first successful application of the MADRL framework for the MANET routing problem that demonstrates a high degree of scalability and robustness even under environments that are outside the trained range of scenarios. This implies that our MARL-based DeepCQ+ design solution significantly improves the performance of Q-learning based CQ+ baseline approach for comparison and increases its practicality and explainability because the real-world MANET environment will likely vary outside the trained range of MANET scenarios. Additional techniques to further increase the gains in performance and scalability are discussed.