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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