# From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing

arXiv: https://arxiv.org/abs/2608.20423 · HTML: https://mlsanity.com/paper/2608.20423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [A Review of Deep Reinforcement Learning for Smart Building Energy Management](https://mlsanity.com/paper/2008.05074/) (2020)
- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)
- [Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec](https://mlsanity.com/paper/2111.00199/) (2021)

## Abstract (arXiv)

Personalised thermal comfort is essential for occupant wellbeing and for the development of more responsive building-control strategies, yet conventional Heating, Ventilation, and Air Conditioning (HVAC) systems rely on static setpoints and population-level comfort models that fail to capture individual physiological variability. This paper presents a two-stage personalised thermal comfort approach integrating multimodal physiological and environmental sensing with reinforcement learning-based decision-making.