# How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning

arXiv: https://arxiv.org/abs/2510.02265 · HTML: https://mlsanity.com/paper/2510.02265/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.05766/) (2019)
- [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)

## Built on by (most cited first, 1 of 1)

- [Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.16813/) (2025)

## Abstract (arXiv)

This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmissions. The transmitter-receiver pair learns to avoid jamming and optimize throughput over time (without prior knowledge of channel conditions or jamming strategies) by using reinforcement learning (RL) to adapt transmit power, modulation, and channel selection. Q-learning is employed for discrete jamming-event states, while Deep Q-Networks (DQN) are employed for continuous states based on received power. Through different reward functions and action sets, the results show that RL can adapt rapidly to spectrum dynamics and sustain high rates as channels and jamming policies change over time.