Offline scheduling in Time Sensitive Networking (TSN) utilizing the Time Aware Shaper (TAS) facilitates optimal deterministic latency and jitter-bounds calculation for Time- Triggered (TT) flows. However, the dynamic nature of traffic in industrial settings necessitates a strategy for adaptively scheduling flows without interrupting existing schedules. Our research identifies critical gaps in current dynamic scheduling methods for TSN and introduces the novel GCN-TD3 approach. This novel approach utilizes a Graph Convolutional Network (GCN) for representing the various relations within different components of TSN and employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to dynamically schedule any incoming flow. Additionally, an Integer Linear Programming (ILP) based offline scheduler is used both to initiate the simulation and serve as a fallback mechanism. This mechanism is triggered to recalculate the entire schedule when the predefined threshold of Gate Control List(GCL) length exceeds. Comparative analyses demonstrate that GCN-TD3 outperforms existing methods like Deep Double Q-Network (DDQN) and Deep Deterministic Policy Gradient (DDPG), exhibiting convergence within 4000 epochs with a 90\% dynamic TT flow admission rate while maintaining deadlines and reducing jitter to as low as 2us. Finally, two modules were developed for the OMNeT++ simulator, facilitating dynamic simulation to evaluate the methodology.

翻译：在时间敏感网络（TSN）中利用时间感知整形器（TAS）进行离线调度，可以为时间触发（TT）流计算最优的确定性延迟和抖动边界。然而，工业场景中流量的动态特性要求在不中断现有调度的情况下实现流调度策略的适应性调整。本研究识别了当前TSN动态调度方法中的关键空白，并提出了一种新颖的GCN-TD3方法。该方法利用图卷积网络（GCN）表示TSN不同组件间的多重关系，并采用双延迟深度确定性策略梯度（TD3）算法对任意到达流进行动态调度。此外，基于整数线性规划（ILP）的离线调度器既用于初始化仿真，也作为回退机制：当门控列表（GCL）长度超过预设阈值时，该机制被触发以重新计算整个调度方案。对比分析表明，GCN-TD3方法优于现有Deep Double Q-Network（DDQN）和Deep Deterministic Policy Gradient（DDPG）等方法，在4000个训练周期内收敛，动态TT流接纳率达到90%，同时满足截止时间要求并将抖动降至2微秒。最后，为OMNeT++仿真器开发了两个模块，支持动态仿真以评估该方法。