Generalizing HVAC Control With Domain Randomized Reinforcement Learning

• URL: http://arxiv.org/abs/2609.05822v1
• Date: Sat, 05 Sep 2026 02:36:28 GMT
• Title: Generalizing HVAC Control With Domain Randomized Reinforcement Learning
• Abstract summary: NOMAD-RL is a general-purpose Reinforcement Learning (RL) controller designed to transfer across heterogeneous thermal zones.<n>Our main contribution is an adaptive domain randomization scheme based on physics-informed normalizing flows.<n>We evaluate NOMAD-RL against a constant-setpoint controller, RL without domain randomization, and MPC in single- and multi-zone settings.
• Score: 6.460844680543445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying advanced HVAC (Heating, Ventilation and Air Conditioning) controllers at scale remains difficult because performance often depends on accurate building models or per-site retuning. We propose NOMAD-RL (Neural Online Meta-Adaptation for Dynamics), a general-purpose Reinforcement Learning (RL) controller designed to transfer across heterogeneous thermal zones through a universal, non-invasive thermostat interface. The controller acts on temperature setpoints from zone measurements and forecasts, while a recurrent policy supports online adaptation under partial observability. Our main contribution is an adaptive domain randomization scheme based on physics-informed normalizing flows, which models correlated and multimodal distributions of thermal-zone parameters while maintaining physical plausibility and controllability. This produces a realistic and progressively adaptive training curriculum that improves transfer across buildings. We evaluate NOMAD-RL against a constant-setpoint PID controller, RL without domain randomization, and MPC in single- and multi-zone settings. NOMAD-RL consistently outperforms the PID and non-randomized RL baselines, and approaches the performance of a well-tuned MPC, especially in the more challenging multi-zone case. These results highlight the potential of adaptive, physics-informed domain randomization for robust and transferable HVAC control.

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