Time Slotted Channel Hopping (TSCH) behavioural mode has been introduced in IEEE 802.15.4e standard to address the ultra-high reliability and ultra-low power communication requirements of Industrial Internet of Things (IIoT) networks. Scheduling the packet transmissions in IIoT networks is a difficult task owing to the limited resources and dynamic topology. In this paper, we propose a phasic policy gradient (PPG) based TSCH schedule learning algorithm. The proposed PPG based scheduling algorithm overcomes the drawbacks of totally distributed and totally centralized deep reinforcement learning-based scheduling algorithms by employing the actor-critic policy gradient method that learns the scheduling algorithm in two phases, namely policy phase and auxiliary phase.
@article{arxiv.2112.01361,
title = {Phasic Policy Gradient Based Resource Allocation for Industrial Internet of Things},
author = {Lokesh Bommisetty and TG Venkatesh},
journal= {arXiv preprint arXiv:2112.01361},
year = {2021}
}