工业控制的预训练大型语言模型

摘要

在工业控制领域，开发具有少样本和低技术债务的高性能控制器具有吸引力。基础模型具有丰富的先验知识，通过与互联网规模语料库的预训练获得，有潜力成为一个具有适当提示的良好控制器。本文以暖通空调（HVAC，Heating, Ventilation, and Air Conditioning）建筑控制为例，检验了GPT-4（一流基础模型之一）作为控制器的能力。为了控制HVAC，我们将任务包装为一种语言游戏，通过提供包括任务简要描述、几个选定演示以及每一步对GPT-4的当前观察的文本，并执行GPT-4响应的动作。我们进行了一系列实验来回答以下问题：1）GPT-4在HVAC控制方面表现如何？2）GPT-4在HVAC控制的不同场景中能否很好地泛化？3）文本上下文的不同部分如何影响性能？总体而言，我们发现GPT-4在少样本和低技术债务情况下实现了与强化学习方法相媲美的性能，表明直接应用基础模型到工业控制任务具有潜力。

English

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.

工业控制的预训练大型语言模型

Pre-Trained Large Language Models for Industrial Control

摘要

Support