# HVAC-DPT: A Decision Pretrained Transformer for HVAC Control

arXiv: https://arxiv.org/abs/2411.19746 · HTML: https://mlsanity.com/paper/2411.19746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [In-context Reinforcement Learning with Algorithm Distillation](https://mlsanity.com/paper/2210.14215/) (2022)
- [Supervised Pretraining Can Learn In-Context Reinforcement Learning](https://mlsanity.com/paper/2306.14892/) (2023)
- [Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining](https://mlsanity.com/paper/2310.08566/) (2023)
- [Emergence of In-Context Reinforcement Learning from Noise Distillation](https://mlsanity.com/paper/2312.12275/) (2023)
- [In-Context Reinforcement Learning for Variable Action Spaces](https://mlsanity.com/paper/2312.13327/) (2023)
- [Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning](https://mlsanity.com/paper/2406.05064/) (2024)
- [Decision Transformer as a Foundation Model for Partially Observable Continuous Control](https://mlsanity.com/paper/2404.02407/) (2024)

## Built on by (most cited first, 1 of 1)

- [Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control](https://mlsanity.com/paper/2607.12856/) (2026)

## Abstract (arXiv)

Building operations consume approximately 40% of global energy, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for up to 50% of this consumption. As HVAC energy demands are expected to rise, optimising system efficiency is crucial for reducing future energy use and mitigating climate change. Existing control strategies lack generalisation and require extensive training and data, limiting their rapid deployment across diverse buildings. This paper introduces HVAC-DPT, a Decision-Pretrained Transformer using in-context Reinforcement Learning (RL) for multi-zone HVAC control. HVAC-DPT frames HVAC control as a sequential prediction task, training a causal transformer on interaction histories generated by diverse RL agents. This approach enables HVAC-DPT to refine its policy in-context, without modifying network parameters, allowing for deployment across different buildings without the need for additional training or data collection. HVAC-DPT reduces energy consumption in unseen buildings by 45% compared to the baseline controller, offering a scalable and effective approach to mitigating the increasing environmental impact of HVAC systems.