Article: Cooperative scheduling of rural public resources based on multi-agent proximal policy optimisation Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.7 pp.12 - 20 Abstract: To address the inefficiency in allocating public resources within rural areas, this study proposes an innovative collaborative scheduling framework based on multi-agent proximal policy optimisation. This approach employs a decentralised decision-making mechanism, enabling multiple agents to learn cooperative strategies thereby optimising the scheduling of shared water and energy resources among interconnected villages. Experimental results demonstrate that this approach effectively captures demand characteristics across diverse rural regions, coordinating the rational exchange of public resources. Specifically, compared to single-agent deep reinforcement learning algorithms, the proposed solution reduces total operational costs by 1.72%, and enhances renewable energy consumption, delivers superior performance in both economic and environmental benefits. These findings validate the broad prospects of multi-agent reinforcement learning as a practical solution for automated intelligent scheduling of rural public resources. Inderscience Publishers - linking academia, business and industry through research

Title: Cooperative scheduling of rural public resources based on multi-agent proximal policy optimisation

Authors: Yanhong Wang; Junqing Sheng

Addresses: College of Economics and Management, Shandong Huayu University of Technology, Dezhou, 253034, China ' College of Economics and Management, Shandong Huayu University of Technology, Dezhou, 253034, China

Abstract: To address the inefficiency in allocating public resources within rural areas, this study proposes an innovative collaborative scheduling framework based on multi-agent proximal policy optimisation. This approach employs a decentralised decision-making mechanism, enabling multiple agents to learn cooperative strategies thereby optimising the scheduling of shared water and energy resources among interconnected villages. Experimental results demonstrate that this approach effectively captures demand characteristics across diverse rural regions, coordinating the rational exchange of public resources. Specifically, compared to single-agent deep reinforcement learning algorithms, the proposed solution reduces total operational costs by 1.72%, and enhances renewable energy consumption, delivers superior performance in both economic and environmental benefits. These findings validate the broad prospects of multi-agent reinforcement learning as a practical solution for automated intelligent scheduling of rural public resources.

Keywords: rural resource management; multi-agent reinforcement learning; MARL; proximal policy optimisation.

DOI: 10.1504/IJRIS.2026.151417

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.7, pp.12 - 20

Received: 13 Oct 2025
Accepted: 05 Nov 2025
Published online: 28 Jan 2026 *

Title: Cooperative scheduling of rural public resources based on multi-agent proximal policy optimisation

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