---
title: "Modular Deep Reinforcement Learning for Multi-Workload Offloading in Edge Networks"
authors:
  - "Hongchang Ke"
  - "Yan Ding"
  - "Lin Pan"
  - "Yang Chen"
  - "Jia Zhao"
doi: "10.24963/ijcai.2025/615"
url: "https://ijcai.org/proceedings/2025/615"
pdf: "https://ijcai.org/proceedings/2025/0615.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "5527-5535"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Representation learning"
  - "Machine Learning: ML: Reinforcement learning"
  - "Data Mining: DM: Networks"
  - "Machine Learning: ML: Applications"
---

# Modular Deep Reinforcement Learning for Multi-Workload Offloading in Edge Networks

Hongchang Ke, Yan Ding, Lin Pan, Yang Chen, Jia Zhao

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 5527-5535. DOI: [10.24963/ijcai.2025/615](https://doi.org/10.24963/ijcai.2025/615)

## Abstract

Dynamic edge networks revolutionize mobile edge computing by enabling real-time applications in intelligent transportation, augmented reality, and industrial Internet of Things (IoT). Efficient workload offloading in dynamic edge networks is crucial for addressing the increasing demands of time-varying workloads while contending with limited computational and communication resources. Existing deep reinforcement learning (DRL)-based offloading decision-making schemes are inadequate for managing scenarios involving multiple workloads and edge servers, particularly when faced with time-varying workload arrivals and fluctuating channel states. To this end, we propose a flexible module weighted fusion DRL framework (DRL-MWF) for scalable and robust multi-workload offloading in edge environments. Unlike traditional monolithic networks, DRL-MWF employs a weighted fusion modular architecture that adapts flexibly to diverse workload distributions. Specifically, DRL-MWF introduces a state representation and normalization strategy to model state and workload characteristics, enabling precise and adaptive decision-making. Furthermore, we design two key mechanisms: a weighted policy correction method to stabilize learning and a prioritized experience replay with weighted importance sampling to accelerate convergence by emphasizing critical transitions. Extensive evaluations on real-world datasets demonstrate that DRL-MWF consistently outperforms state-of-the-art baselines. These results reveal DRL-MWF's potential to transform workload offloading in next-generation edge computing systems, ensuring high performance in dynamic scenarios.