{"type":"ScholarlyArticle","title":"Modular Deep Reinforcement Learning for Multi-Workload Offloading in Edge Networks","authors":[{"name":"Hongchang Ke","slug":"hongchang-ke","given":"Hongchang","family":"Ke","affiliation":""},{"name":"Yan Ding","slug":"yan-ding","given":"Yan","family":"Ding","affiliation":""},{"name":"Lin Pan","slug":"lin-pan","given":"Lin","family":"Pan","affiliation":""},{"name":"Yang Chen","slug":"yang-chen","given":"Yang","family":"Chen","affiliation":""},{"name":"Jia Zhao","slug":"jia-zhao","given":"Jia","family":"Zhao","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/615","url":"https://ijcai.org/proceedings/2025/615","pdf":"https://ijcai.org/proceedings/2025/0615.pdf","markdown":"https://ijcai.org/proceedings/2025/615.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/615","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":615,"pages":{"first":5527,"last":5535},"keywords":["Machine Learning: ML: Representation learning","Machine Learning: ML: Reinforcement learning","Data Mining: DM: Networks","Machine Learning: ML: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}