Research on Fire Emergency Evacuation Simulation Based on Cooperative Deep Reinforcement Learning

Authors

Lingjia Ni, 1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;Follow
Xiaoxia Huang, 1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;3.Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518034, China;Follow
Hongga Li, 1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
Zibo Zhang, 1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;

Abstract

Abstract: The fire accident is a major threat to the public safety, in which the high temperature, toxic and harmful gases seriously interfer the selection of the evacuation routes. Deep reinforcement learning is introduced into the research of emergency evacuation simulation, and a cooperative double deep Q network algorithm is proposed for the multi-agent environment. A fire scene model that changes dynamically over time is established to provide the real-time information on the distribution of the dangerous areas for the evacuation. The independent agent neural networks are integrated and the multi-agent unified deep neural network is established to realize the sharing of the neural network and experience among all agents, and improve the overall cooperative evacuation efficiency. The experimental comparison results show that the proposed method has the good stability and adaptability, improved training and learning efficiency, and good application value.

Recommended Citation

Ni, Lingjia; Huang, Xiaoxia; Li, Hongga; and Zhang, Zibo (2022) "Research on Fire Emergency Evacuation Simulation Based on Cooperative Deep Reinforcement Learning," Journal of System Simulation: Vol. 34: Iss. 6, Article 18.
DOI: 10.16182/j.issn1004731x.joss.21-0108
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss6/18

First Page

1353

Revised Date

2021-05-18

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0108

Last Page

1366

CLC

TP391.9

Recommended Citation

Lingjia Ni, Xiaoxia Huang, Hongga Li, Zibo Zhang. Research on Fire Emergency Evacuation Simulation Based on Cooperative Deep Reinforcement Learning[J]. Journal of System Simulation, 2022, 34(6): 1353-1366.

Corresponding Author

Xiaoxia Huang,huangxx@aircas.ac.cn

DOI

10.16182/j.issn1004731x.joss.21-0108