  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.
