Machine Learning Based Markov Decision Framework for Optimizing Circular Economy Systems

Daozheng Qu, Yanfei Ma

Abstract: The increasing demand for sustainable development necessitates sophisticated computational frameworks that facilitate intricate, long-term decision-making in circular economy systems. Therefore, we developed a novel machine learning based Markov decision framework that effectively combines predictive analytics with sequential decision theory to provide adaptive and scalable optimization of resource circulation processes. The developed architecture utilizes supervised learning to predict material flow dynamics and exploits the Markov decision process (MDP) to develop reward-sensitive strategies under environmental variability and operational uncertainty. The study results on municipal waste management demonstrate the framework’s superior performance in resource recovery efficiency, policy responsiveness, and sustainability impact compared to traditional static or rule-based methods. Such findings contribute to the development of a generalizable, learning-enabled tool for dynamic multi-stage decision assistance in complex and unpredictable circular economy contexts.