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Application of multi-agent reinforcement learning system in optimizing higher education resource allocation

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This framework demonstrates improved resource allocation and efficiency in higher education scenarios, highlighting dynamic adaptability.

The GMAPPO framework enhances decision-making efficiency in higher education, achieving resource utilization rates of 73.20% and 95.07%.
Experimental results show a policy compliance rate of 98.5%, indicating high effectiveness for resource allocation strategies.
Implemented using a centralized training and distributed execution approach, the model integrates local autonomy with global perceptions for better outcomes.
The system achieves a response time of only 3.2 seconds under dynamic conditions, showcasing its adaptability in rapidly changing educational environments.

Xu et al. (2025) studied this question.

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