Deep Reinforcement Learning Control of a Boiling Water Reactor

This article presents (nonlinear) control system synthesis for a boiling water reactor (BWR) by using artificial intelligence (AI)-based reinforcement learning (RL), where the pertinent algorithm is deep deterministic policy gradient (DDPG). The BWR model, used in this article, exhibits limit cycling and/or chaotic behavior in different regions of operation. The performance of the RL control system is compared with that of a control system synthesized by the standard H∞ theory. The results of comparison show that the RL control system outperforms the H∞ control system for disturbance rejection, stability under perturbation, and set-point tracking in a majority of the test cases.