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arXiv · 2308.05711

A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control

Marshall Wang·John Willes·Thomas Jiralerspong·Matin Moezzi
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Abstract

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.

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Marshall Wang, John Willes, Thomas Jiralerspong, Matin Moezzi. 2023-08-10. A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control. https://arxiv.org/abs/2308.05711

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