  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.
