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

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

The dynamic scheduling optimization of sports facilities faces challenges posed by real-time demand fluctuations and complex interdependencies between facilities. To address the adaptability limitations of traditional centralized approaches, this study proposes a decentralized multi-agent reinforcem...

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Bibliographic Details
Main Authors: Sen Chen, Xiaolong Chen, Qian Bao, Hongfeng Zhang, Cora Un In Wong
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Buildings
Subjects:
decentralized MARL
graph neural networks
dynamic scheduling
sports buildings
proximal policy optimization
user satisfaction
Online Access:https://www.mdpi.com/2075-5309/15/14/2554
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