Adaptive Multi-Agent Reinforcement Learning with Graph Neural Networks for Dynamic Optimization in Sports Buildings

Computer Science

• Graph Neural Network 100%
• Dynamic Optimization 100%
• Multi-Agent Reinforcement Learning 100%
• Decision-Making 66%
• Learning Framework 33%
• Attention (Machine Learning) 33%
• Simulated Environment 33%
• User Satisfaction 33%
• Communication Overhead 33%
• Experimental Result 33%
• Computational Efficiency 33%
• Centralized Approach 33%
• Operational Efficiency 33%
• Interdependency 33%
• Practical Solution 33%
• Connection Weight 33%
• Resources Management 33%