  The Industrial Internet of Things (IIoT) demands adaptable Networked Embedded
Systems (NES) for optimal performance. Combined with recent advances in
Artificial Intelligence (AI), tailored solutions can be developed to meet
specific application requirements. This study introduces HRL-TSCH, an approach
rooted in Hierarchical Reinforcement Learning (HRL), to devise Time Slotted
Channel Hopping (TSCH) schedules provisioning IIoT demand. HRL-TSCH employs
dual policies: one at a higher level for TSCH schedule link management, and
another at a lower level for timeslot and channel assignments. The proposed RL
agents address a multi-objective problem, optimizing throughput, power
efficiency, and network delay based on predefined application requirements.
Simulation experiments demonstrate HRL-TSCH superiority over existing
state-of-art approaches, effectively achieving an optimal balance between
throughput, power consumption, and delay, thereby enhancing IIoT network
performance.
