title: Pre-Trained Large Language Models for Industrial Control
summary: 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.
paper_authors._id: 64d1a6e83c4a1b39a0298c90
paper_authors.name: Lei Song
paper_authors._id: 64d1a6e83c4a1b39a0298c91
paper_authors.name: Chuheng Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-08T15:19:34.448Z
paper_authors._id: 64c32727c370f29a10334d35
paper_authors.avatarUrl: /avatars/a3284bdd2e51f437433b89a96a904448.svg
paper_authors.fullname: ZhangChuheng
paper_authors.type: user
paper_authors.user: zhangchuheng123
paper_authors._id: 64d1a6e83c4a1b39a0298c92
paper_authors.name: Li Zhao
paper_authors._id: 64d1a6e83c4a1b39a0298c93
paper_authors.name: Jiang Bian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-08T15:19:59.980Z
paper_authors._id: 63f253f8f4e30ffd2bd308fb
paper_authors.avatarUrl: /avatars/303f4c7ee588f638acf78a7966786e1e.svg
paper_authors.fullname: Jiang Bian
paper_authors.type: user
paper_authors.user: bianjiang
paper_title: Pre-Trained Large Language Models for Industrial Control
paper_summary: 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.
paper_ai_keywords: GPT-4 language game short description demonstrations current observation RL methods