Analysis of Model-Agnostic Meta-Reinforcement Learning on Automated HVAC Control

Abstract

This paper introduces a Model-Agnostic Meta-Reinforcement Learning framework for HVAC automation, integrating Model Agnostic Meta-Learning with Double Deep Q-Networks to improve adaptability across varying environmental conditions. The proposed approach is evaluated using Sinergym, an EnergyPlus-integrated RL Simulation framework, and benchmarked against conventional RL-based HVAC controllers. Results demonstrate that Model-Agnostic Meta-Learning integrated Double Deep Q-Network achieves a 7% reduction in overall power consumption while dynamically adapting to climate variations. These findings highlight the potential of Model Agnostic Meta-Learning in optimizing HVAC control strategies.