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

• URL: http://arxiv.org/abs/2308.05711v1
• Date: Thu, 10 Aug 2023 17:20:02 GMT
• Title: A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
• Authors: Marshall Wang, John Willes, Thomas Jiralerspong, Matin Moezzi
• Abstract summary: We benchmark two popular classical and deep RL methods (Q-Learning and Deep-Q-Networks) across multiple HVAC environments. The findings provide insight for configuring RL agents in HVAC systems, promoting energy-efficient and cost-effective operation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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