Phasic Policy Gradient Based Resource Allocation for Industrial Internet of Things

TL;DR

This paper introduces a novel phasic policy gradient algorithm for resource scheduling in IIoT networks, improving upon existing reinforcement learning methods by employing a two-phase learning approach to enhance reliability and efficiency.

Contribution

It proposes a two-phase policy gradient scheduling algorithm for IIoT networks, addressing limitations of existing RL-based methods with a hybrid actor-critic approach.

Findings

Effective scheduling in IIoT networks demonstrated

Improved reliability and power efficiency

Overcomes limitations of prior RL methods

Abstract

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.