# Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation

arXiv: https://arxiv.org/abs/2410.10521 · HTML: https://mlsanity.com/paper/2410.10521/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 16 of 16)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments](https://mlsanity.com/paper/1909.11800/) (2019)
- [Edge Continual Learning for Dynamic Digital Twins over Wireless Networks](https://mlsanity.com/paper/2204.04795/) (2022)
- [A Collaborative Multi-agent Reinforcement Learning Anti-jamming Algorithm in Wireless Networks](https://mlsanity.com/paper/1809.04374/) (2018)
- [DeepWiFi: Cognitive WiFi with Deep Learning](https://mlsanity.com/paper/1910.13315/) (2019)
- [QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.05766/) (2019)
- [Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach](https://mlsanity.com/paper/2004.12539/) (2020)
- [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)

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

- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning](https://mlsanity.com/paper/2510.02265/) (2025)
- [Coordinated Anti-Jamming Resilience in Swarm Networks via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2512.16813/) (2025)

## Abstract (arXiv)

Deep Reinforcement Learning (DRL) has been highly effective in learning from and adapting to RF environments and thus detecting and mitigating jamming effects to facilitate reliable wireless communications. However, traditional DRL methods are susceptible to catastrophic forgetting (namely forgetting old tasks when learning new ones), especially in dynamic wireless environments where jammer patterns change over time. This paper considers an anti-jamming system and addresses the challenge of catastrophic forgetting in DRL applied to jammer detection and mitigation. First, we demonstrate the impact of catastrophic forgetting in DRL when applied to jammer detection and mitigation tasks, where the network forgets previously learned jammer patterns while adapting to new ones. This catastrophic interference undermines the effectiveness of the system, particularly in scenarios where the environment is non-stationary. We present a method that enables the network to retain knowledge of old jammer patterns while learning to handle new ones. Our approach substantially reduces catastrophic forgetting, allowing the anti-jamming system to learn new tasks without compromising its ability to perform previously learned tasks effectively. Furthermore, we introduce a systematic methodology for sequentially learning tasks in the anti-jamming framework. By leveraging continual DRL techniques based on PackNet, we achieve superior anti-jamming performance compared to standard DRL methods. Our proposed approach not only addresses catastrophic forgetting but also enhances the adaptability and robustness of the system in dynamic jamming environments. We demonstrate the efficacy of our method in preserving knowledge of past jammer patterns, learning new tasks efficiently, and achieving superior anti-jamming performance compared to traditional DRL approaches.