# Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.16813 · HTML: https://mlsanity.com/paper/2512.16813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.05766/) (2019)
- [Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks](https://mlsanity.com/paper/2103.14056/) (2021)
- [Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.04477/) (2021)
- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning](https://mlsanity.com/paper/2510.02265/) (2025)

## Abstract (arXiv)

Reactive jammers pose a severe security threat to robotic-swarm networks by selectively disrupting inter-agent communications and undermining formation integrity and mission success. Conventional countermeasures such as fixed power control or static channel hopping are largely ineffective against such adaptive adversaries. This paper presents a multi-agent reinforcement learning (MARL) framework based on the QMIX algorithm to improve the resilience of swarm communications under reactive jamming. We consider a network of multiple transmitter-receiver pairs sharing channels while a reactive jammer with Markovian threshold dynamics senses aggregate power and reacts accordingly. Each agent jointly selects transmit frequency (channel) and power, and QMIX learns a centralized but factorizable action-value function that enables coordinated yet decentralized execution. We benchmark QMIX against a genie-aided optimal policy in a no-channel-reuse setting, and against local Upper Confidence Bound (UCB) and a stateless reactive policy in a more general fading regime with channel reuse enabled. Simulation results show that QMIX rapidly converges to cooperative policies that nearly match the genie-aided bound, while achieving higher throughput and lower jamming incidence than the baselines, thereby demonstrating MARL's effectiveness for securing autonomous swarms in contested environments.