Reinforcement learning (RL) is a promising approach for optimizing HVAC control. RL offers a framework for improving system performance, reducing energy consumption, and enhancing cost efficiency. We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments and explore the practical consideration of model hyper-parameter selection and reward tuning. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.
@article{arxiv.2308.05711,
title = {A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control},
author = {Marshall Wang and John Willes and Thomas Jiralerspong and Matin Moezzi},
journal= {arXiv preprint arXiv:2308.05711},
year = {2023}
}