@article{song2023pre-trained,
author = {Song, Lei and Zhang, Chuheng and Zhao, Li and Bian, Jiang},
title = {Pre-Trained Large Language Models for Industrial Control},
year = {2023},
month = {August},
abstract = {For industrial control, developing high-performance controllers with few samples and low technical debt is appealing. Foundation models, possessing rich prior knowledge obtained from pre-training with Internet-scale corpus, have the potential to be a good controller with proper prompts. In this paper, we take HVAC (Heating, Ventilation, and Air Conditioning) building control as an example to examine the ability of GPT-4 (one of the first-tier foundation models) as the controller. To control HVAC, we wrap the task as a language game by providing text including a short description for the task, several selected demonstrations, and the current observation to GPT-4 on each step and execute the actions responded by GPT-4. We conduct series of experiments to answer the following questions: 1)~How well can GPT-4 control HVAC? 2)~How well can GPT-4 generalize to different scenarios for HVAC control? 3) How different parts of the text context affect the performance? In general, we found GPT-4 achieves the performance comparable to RL methods with few samples and low technical debt, indicating the potential of directly applying foundation models to industrial control tasks.},
url = {http://approjects.co.za/?big=en-us/research/publication/pre-trained-large-language-models-for-industrial-control/},
journal = {ArXiv},
volume = {abs/2308.03028},
}