Generalizing HVAC Control With Domain Randomized Reinforcement Learning
NOMAD-RL introduces a general-purpose RL controller for HVAC systems that transfers across heterogeneous thermal zones via a universal, non-invasive thermostat interface The core innovation is an adaptive domain randomization scheme using physics-informed normalizing flows to model correlated, multimodal distributions of thermal-zone parameters while preserving physical plausibility A recurrent policy enables online meta-adaptation under partial observability, allowing the controller to adjust i
Analysis
TL;DR
- NOMAD-RL introduces a general-purpose RL controller for HVAC systems that transfers across heterogeneous thermal zones via a universal, non-invasive thermostat interface
- The core innovation is an adaptive domain randomization scheme using physics-informed normalizing flows to model correlated, multimodal distributions of thermal-zone parameters while preserving physical plausibility
- A recurrent policy enables online meta-adaptation under partial observability, allowing the controller to adjust in real-time without per-site retuning
- NOMAD-RL outperforms constant-setpoint PID and non-randomized RL baselines, approaching well-tuned MPC performance, particularly in multi-zone settings
- The work demonstrates that adaptive, physics-informed domain randomization can produce realistic, progressively adaptive training curricula that significantly improve cross-building transfer
Why It Matters
This research addresses a critical bottleneck in deploying AI-driven building automation at scale: the reliance on accurate building models and costly per-site controller retuning. By enabling a single RL controller to generalize across diverse thermal zones through a non-invasive interface, NOMAD-RL offers a practical pathway for energy-efficient HVAC optimization in real-world deployments without requiring extensive infrastructure changes or expert tuning.
Technical Details
- NOMAD-RL Architecture: A recurrent policy-based reinforcement learning controller that operates on temperature setpoints derived from zone measurements and forecasts, supporting online adaptation under partial observability through a universal thermostat interface
- Physics-Informed Normalizing Flows: The adaptive domain randomization scheme employs normalizing flows constrained by physical laws to model correlated and multimodal distributions of thermal-zone parameters, ensuring generated training scenarios remain physically plausible and controllable
- Training Curriculum: The randomization scheme produces a progressively adaptive training curriculum that evolves during training, exposing the controller to increasingly diverse and realistic building dynamics
- Evaluation Setup: Benchmarked against constant-setpoint PID, RL without domain randomization, and model predictive control (MPC) in both single-zone and multi-zone configurations
- Performance Results: NOMAD-RL consistently surpasses PID and non-randomized RL baselines, with multi-zone performance approaching that of a well-tuned MPC, demonstrating the value of adaptive domain randomization for robust transfer
Industry Insight
- The non-invasive thermostat interface design lowers deployment barriers for building operators, enabling AI-driven control upgrades without replacing existing HVAC hardware—making it commercially viable for large-scale retrofits across commercial and residential portfolios
- Physics-informed domain randomization represents a transferable paradigm beyond HVAC; any RL application requiring simulation-to-reality transfer with physical constraints (robotics, autonomous vehicles, industrial automation) could benefit from similar adaptive curriculum approaches
- As energy efficiency regulations tighten globally, controllers like NOMAD-RL that reduce per-site tuning costs while approaching MPC-level performance could accelerate adoption of advanced control strategies in the built environment, potentially yielding significant aggregate energy savings
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