Article: A multi-agent reinforcement learning approach to heterogeneous resource allocation in lifelong education Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.7 pp.21 - 37 Abstract: Lifelong education faces challenges in resource allocation due to heterogeneity, dynamism, and scalability. This study proposes a distributed allocation model using multi-agent reinforcement learning (MARL), where learners and providers act as autonomous agents. Employing a centralised training with decentralised execution (CTDE) paradigm, the model applies the multi-agent deep deterministic policy gradient algorithm for collaborative learning. A composite reward function integrates user quality of experience (QoE), system cost, and fairness. A tripartite stochastic game model theoretically confirms the existence of a Nash equilibrium. Simulations show the model outperforms baseline algorithms, achieving superior overall system utility (37.1% higher), average quality of experience (41.8% higher), resource utilisation (76.3%), and fairness (Jain's index = 0.89), with strong convergence and adaptability. This provides an efficient, scalable solution for heterogeneous resource management in lifelong education. Inderscience Publishers - linking academia, business and industry through research

Title: A multi-agent reinforcement learning approach to heterogeneous resource allocation in lifelong education

Authors: Miao Wang; Zhen Gu

Addresses: Department of Scientific Research and Planning, The Open University of Jilin, Changchun, 130022, China ' College of Ethnic Preparatory Education, Jilin Provincial Institute of Education, Changchun, 130022, China

Abstract: Lifelong education faces challenges in resource allocation due to heterogeneity, dynamism, and scalability. This study proposes a distributed allocation model using multi-agent reinforcement learning (MARL), where learners and providers act as autonomous agents. Employing a centralised training with decentralised execution (CTDE) paradigm, the model applies the multi-agent deep deterministic policy gradient algorithm for collaborative learning. A composite reward function integrates user quality of experience (QoE), system cost, and fairness. A tripartite stochastic game model theoretically confirms the existence of a Nash equilibrium. Simulations show the model outperforms baseline algorithms, achieving superior overall system utility (37.1% higher), average quality of experience (41.8% higher), resource utilisation (76.3%), and fairness (Jain's index = 0.89), with strong convergence and adaptability. This provides an efficient, scalable solution for heterogeneous resource management in lifelong education.

Keywords: multi-agent reinforcement learning; MARL; resource allocation; lifelong education; heterogeneous networks; stochastic games.

DOI: 10.1504/IJICT.2026.151565

International Journal of Information and Communication Technology, 2026 Vol.27 No.7, pp.21 - 37

Received: 29 Sep 2025
Accepted: 30 Oct 2025
Published online: 06 Feb 2026 *

Title: A multi-agent reinforcement learning approach to heterogeneous resource allocation in lifelong education

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