A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
This work addresses energy consumption and cost issues in HVAC systems for building operators, but it is incremental as it compares existing methods without introducing new techniques.
The paper tackled the problem of optimizing HVAC control by benchmarking classical and deep reinforcement learning methods, finding that these approaches can improve energy efficiency and cost-effectiveness in HVAC systems.
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.