  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.
