Graph Neural Network-Driven Adaptive MARL with Meta-Learning for Resilient Dynamic Optimization of Sports Building Facilities

We propose a decentralized multi-agent reinforcement learning (MARL) framework enhanced with graph neural networks (GNNs) for dynamic scheduling optimization in sports buildings, addressing the limitations of conventional centralized approaches. The system models each facility as an autonomous agent that learns adaptive policies through proximal policy optimization, where local observations include real-time occupancy, energy consumption, and user preferences. Global coordination is achieved via a GNN-based communication protocol, which captures spatial–temporal dependencies between facilities by treating the building as a graph with nodes representing facilities and edges encoding connectivity. The framework incorporates prioritized experience replay to handle unexpected events such as equipment failures or demand spikes, ensuring robust adaptation in simulated real-world scenarios. Moreover, the reward function dynamically balances occupancy utilization, energy efficiency, and user satisfaction with adaptive weights adjusted through meta-learning. The proposed method interfaces with existing facility management systems by translating agent actions into control signals for HVAC, lighting, and staff scheduling. Implemented using Vision-Transformer hybrids and GraphSAGE architectures, the framework supports federated learning to preserve data privacy across facilities. Experimental results in simulation demonstrate significant improvements, suggesting strong potential for deployment in large-scale, heterogeneous environments. This work advances the state-of-the-art in intelligent building management by enabling decentralized decision-making while maintaining global coordination through graph-structured communication.