工業控制的預訓練大型語言模型

摘要

對於工業控制而言，開發具有少量樣本和低技術債務的高性能控制器具有吸引力。基礎模型具有豐富的先前知識，通過與互聯網規模語料庫的預訓練獲得，有潛力成為一個具有適當提示的良好控制器。本文以暖通空調（Heating, Ventilation, and Air Conditioning，HVAC）建築控制為例，檢驗了GPT-4（一級基礎模型之一）作為控制器的能力。為了控制HVAC，我們將任務包裝成一種語言遊戲，通過提供包括任務簡短描述、幾個選定示範以及每一步對GPT-4的當前觀察的文本，並執行GPT-4回應的操作。我們進行了一系列實驗以回答以下問題：1）GPT-4能夠控制HVAC的效果如何？2）GPT-4能夠對HVAC控制的不同情境進行很好的泛化嗎？3）文本上下文的不同部分如何影響性能？總的來說，我們發現GPT-4實現了與少量樣本和低技術債務的RL方法相當的性能，表明直接應用基礎模型於工業控制任務具有潛力。

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.

工業控制的預訓練大型語言模型

Pre-Trained Large Language Models for Industrial Control

摘要

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