Study on pedestrian evacuation model based on reinforcement learning

Zhu Rui, Hu Jun, Fan Ling, Zhang Qi and Wei Juan
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Zhu Rui: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, P. R. China
Hu Jun: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, P. R. China†Key Laboratory of Interior Layout Optimization and Security, Institutions of Higher Education of Sichuan Province, Chengdu Normal University, Chengdu 611130, P. R. China
Fan Ling: ��Key Laboratory of Interior Layout Optimization and Security, Institutions of Higher Education of Sichuan Province, Chengdu Normal University, Chengdu 611130, P. R. China
Zhang Qi: ��School of Intelligent Manufacturing, Panzhihua University, Panzhihua 617000, P. R. China
Wei Juan: ��Key Laboratory of Interior Layout Optimization and Security, Institutions of Higher Education of Sichuan Province, Chengdu Normal University, Chengdu 611130, P. R. China§Key Laboratory of Multidimensional Data Sensing and Intelligent Information Processing of Dazhou Key Laboratory, Dazhou 635000, P. R. China¶Key Laboratories of Sensing and Application of Intelligent Optoelectronic System in Sichuan Provincial Universities, Dazhou 635000, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 07, 1-18

Abstract: This paper proposed a pedestrian evacuation model combined with reinforcement learning in order to study how to better guide pedestrians to complete evacuation in specific indoor scenes. This model introduced the way of establishing a scene in cellular automata and formulated reward rules according to the characteristics of the scene. It fitted the psychological activities of pedestrians in the actual evacuation process and trained the strategy of pedestrians at the overall level through the Q-learning algorithm from the reinforcement learning area. A speed control mechanism combined with real statistical data was introduced to simulate the speed attenuation. A simulation platform was built to compare the evacuation conditions under different scenarios and the different total numbers of pedestrians. The research showed that the model could automatically realize the exit selection function of pedestrians and part of conformity behavior. In the same evacuation scenario, this model could show adaptability for the different total numbers of pedestrians.

Keywords: Pedestrian evacuation model; cellular automata model; reinforcement learning; Q-learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124502474

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