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

08/10/2023

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by Marshall Wang, et al.

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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.

Marshall Wang
1 publication
John Willes
3 publications
Thomas Jiralerspong
2 publications
Matin Moezzi
1 publication

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