Context-Aware Meta-Reinforcement Learning for Intelligent Diverse Indoor HVAC Control

Ensuring high indoor air quality (IAQ) while minimizing energy consumption and preserving occupant comfort is a central challenge in building management systems. Traditional rule-based or single-objective controls often neglect dynamic fluctuations in pollution levels and occupant behavior, leading to suboptimal trade-offs. In this paper, we propose a context-aware Meta-Reinforcement Learning (Meta-RL) framework that simultaneously addresses multiple objectives-IAQ, energy efficiency, and comfort-under a variety of building configurations and disturbances (e.g., wildfires, equipment faults, occupancy surges). Our approach integrates a Transformer-based encoder for latent context extraction, a Meta-Pareto hypernetwork that generates diverse policies for user-driven preferences, and safety-constrained adaptation to maintain strict pollutant thresholds. Through extensive simulations using EnergyPlus and real-world calibration data, the proposed framework demonstrates (1) significantly lower IAQ violations compared to standard RL and rule-based baselines, (2) reduced energy usage while maintaining comfortable thermal conditions, and (3) rapid transfer to new buildings via few-shot meta-training. These findings underscore the potential of Meta-RL to deliver robust, flexible HVAC control solutions in complex, real-world indoor environments.