Pre-Trained Large Language Models for Industrial Control
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.
64d1a6e83c4a1b39a0298c90
Lei Song
64d1a6e83c4a1b39a0298c91
Chuheng Zhang
admin_assigned
2023-08-08T15:19:34.448Z
64c32727c370f29a10334d35
/avatars/a3284bdd2e51f437433b89a96a904448.svg
ZhangChuheng
user
zhangchuheng123
64d1a6e83c4a1b39a0298c92
Li Zhao
64d1a6e83c4a1b39a0298c93
Jiang Bian
admin_assigned
2023-08-08T15:19:59.980Z
63f253f8f4e30ffd2bd308fb
/avatars/303f4c7ee588f638acf78a7966786e1e.svg
Jiang Bian
user
bianjiang
Pre-Trained Large Language Models for Industrial Control
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.
GPT-4 demonstrates competitive HVAC control performance with minimal samples and technical debt, suggesting potential for direct application to industrial control tasks.
GPT-4 HVAC language game RL methods