In the era of sixth generation mobile networks (6G), industrial big data is rapidly generated due to the increasing data‐driven applications in the Industrial Internet of Things (IIoT). Effectively processing such data, for example, knowledge learning, on resource‐limited IIoT devices becomes a challenge. To this end, we introduce a cloud‐edge‐end collaboration architecture, in which computing, communication, and storage resources are flexibly coordinated to alleviate the issue of resource constraints. To achieve better performance in hyper‐connected experience, real‐time communication, and sustainable computing, we construct a novel architecture combining digital twin (DT)‐IIoT with edge networks. In addition, considering the energy consumption and delay issues in distributed learning, we propose a deep reinforcement learning‐based method called deep deterministic policy gradient with double actors and double critics (D4PG) to manage the multi‐dimensional resources, that is, CPU cycles, DT models, and communication bandwidths, enhancing the exploration ability and improving the inaccurate value estimation of agents in continuous action spaces. In addition, we introduce a synchronization threshold for distributed learning framework to avoid the synchronization latency caused by stragglers. Extensive experimental results prove that the proposed architecture can efficiently conduct knowledge learning, and the intelligent scheme can also improve system efficiency by managing multi‐dimensional resources.

中文翻译：

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具有深度确定性政策梯度的多维资源管理，适用于第六代数字孪生工业物联网

第六代移动网络（6G）时代，工业物联网（IIoT）中数据驱动的应用不断增加，工业大数据迅速产生。在资源有限的工业物联网设备上有效处理此类数据（例如知识学习）成为一项挑战。为此，我们引入云边端协同架构，灵活协调计算、通信、存储资源，缓解资源约束问题。为了在超连接体验、实时通信和可持续计算方面实现更好的性能，我们构建了一种将数字孪生（DT）-IIoT 与边缘网络相结合的新颖架构。此外，考虑到分布式学习中的能耗和延迟问题，我们提出了一种基于深度强化学习的方法，称为具有双参与者和双批评者的深度确定性策略梯度（D4PG）来管理多维资源，即CPU周期、DT模型和通信带宽，增强探索能力，改善连续动作空间中智能体价值估计不准确的问题。此外，我们为分布式学习框架引入了同步阈值，以避免落后者造成的同步延迟。大量的实验结果证明，所提出的架构可以有效地进行知识学习，并且智能方案还可以通过管理多维资源来提高系统效率。