Sinergym is an open-source, Gymnasium-compatible virtual testbed that standardizes building energy optimization experiments with 87 preconfigured environments, weather variability, and experiment tracking.
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
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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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SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning
Sinergym is an open-source, Gymnasium-compatible virtual testbed that standardizes building energy optimization experiments with 87 preconfigured environments, weather variability, and experiment tracking.