Driven by the demand of time-sensitive and data-intensive applications, edge computing has attracted wide attention as one of the cornerstones of modern service architectures. An edge-based system can facilitate a flexible processing of tasks over heterogeneous resources. Hence, computation offloading is the key technique for systematic service improvement. However, with the proliferation of devices, traditional approaches have clear limits in handling dynamic and heterogeneous systems at scale. Deep Reinforcement Learning (DRL), as a promising alternative, has shown great potential with powerful high-dimensional perception and decision-making capability to enable intelligent offloading, but the great complexity in DRL-based algorithm design turns out to be an obstacle. In light of this, this survey provides a comprehensive view of DRL-based approaches to computation offloading in edge computing systems. We cover state-of-the-art advances by delving into the fundamental elements of DRL algorithm design with focuses on the target environmental factors, Markov Decision Process (MDP) model construction, and refined learning strategies. Based on our investigation, several open challenges are further highlighted from both the perspective of algorithm design and realistic requirements that deserve more attention in future research.

中文翻译：

---

边缘系统中计算卸载的调查：从深度强化学习方法的角度来看


在时间敏感、数据密集型应用需求的推动下，边缘计算作为现代服务架构的基石之一受到广泛关注。基于边缘的系统可以促进异构资源上任务的灵活处理。因此，计算卸载是系统服务改进的关键技术。然而，随着设备的激增，传统方法在大规模处理动态和异构系统方面存在明显的局限性。深度强化学习（DRL）作为一种有前途的替代方案，凭借强大的高维感知和决策能力显示出巨大的潜力，可以实现智能卸载，但基于 DRL 的算法设计的巨大复杂性却成为了障碍。有鉴于此，本次调查提供了边缘计算系统中基于 DRL 的计算卸载方法的全面视图。我们通过深入研究 DRL 算法设计的基本要素，重点关注目标环境因素、马尔可夫决策过程 (MDP) 模型构建和完善的学习策略，来涵盖最先进的进展。根据我们的调查，从算法设计和现实需求的角度进一步突出了一些开放的挑战，值得在未来的研究中更多关注。