# A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networks

arXiv: https://arxiv.org/abs/1809.04374 · HTML: https://mlsanity.com/paper/1809.04374/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 10 of 10)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks](https://mlsanity.com/paper/2103.14056/) (2021)
- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [Learning-Augmented Decentralized Online Convex Optimization in Networks](https://mlsanity.com/paper/2306.10158/) (2023)
- [Anti-Jamming Games in Multi-Band Wireless Ad Hoc Networks](https://mlsanity.com/paper/2111.11178/) (2021)
- [Multi-Agent Adversarial Attacks for Multi-Channel Communications](https://mlsanity.com/paper/2201.09149/) (2022)
- [Network Resource Allocation Strategy Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2202.03193/) (2022)
- [Recurrent Neural Network-based Anti-jamming Framework for Defense Against Multiple Jamming Policies](https://mlsanity.com/paper/2208.09518/) (2022)
- [Strangeness-driven Exploration in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.13448/) (2022)
- [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)

In this letter, we investigate the anti-jamming defense problem in multi-user scenarios, where the coordination among users is taken into consideration. The Markov game framework is employed to model and analyze the anti-jamming defense problem, and a collaborative multi-agent anti-jamming algorithm (CMAA) is proposed to obtain the optimal anti-jamming strategy. In sweep jamming scenarios, on the one hand, the proposed CMAA can tackle the external malicious jamming. On the other hand, it can effectively cope with the mutual interference among users. Simulation results show that the proposed CMAA is superior to both sensing based method and independent Q-learning method, and has the highest normalized rate.