Optimal Control of Information Dissemination Based on Model‐Free Reinforcement Learning

ABSTRACT With the proliferation of mobile social networks, controlling information dissemination faces challenges in dynamic environments where state transition models are often unavailable. To address the optimal control problem under such model‐free constraints, this paper aims to minimize the cum...

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Bibliographic Details
Published in:Concurrency and computation Vol. 37; no. 21-22
Main Authors: Xia, Dan, Xu, Enyu, Tian, Yuan, Min, Qiusha
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 25.09.2025
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ISSN:1532-0626, 1532-0634
Online Access:Get full text
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Summary:ABSTRACT With the proliferation of mobile social networks, controlling information dissemination faces challenges in dynamic environments where state transition models are often unavailable. To address the optimal control problem under such model‐free constraints, this paper aims to minimize the cumulative cost of epidemic‐based information dissemination by innovatively integrating temporal difference (TD) learning with real‐time capacity adaptation. The proposed method dynamically identifies optimal control signal timing, eliminating dependency on predefined state matrices. Experimental results demonstrate a 7.0% reduction in cumulative network cost and a 52.6% improvement in s‐controllability compared to dynamic programming baselines. This model‐free framework not only enhances robustness in time‐varying networks but also offers scalability for large‐scale applications, advancing real‐time control in social media analysis and public opinion management.
Bibliography:Funding
This work was supported by the 2021 National Education Science Project Ministry of Education Key Project (Research on Knowledge Dissemination Mechanism and Optimization Strategy of Online Teaching in Convergence Media Environment) (DCA210317).
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.70213