Simulating multi-exit evacuation using deep reinforcement learning

Dong Xu, Xiao Huang, Joseph Mango, Xiang Li, Zhenlong Li

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with large numbers of pedestrians. We propose a multi-exit evacuation simulation based on deep reinforcement learning (DRL), referred to as the MultiExit-DRL, which involves a deep neural network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: varying pedestrian distribution ratios; varying exit width ratios; and varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to a high efficiency of exit utilization.

Original languageEnglish (US)
Pages (from-to)1542-1564
Number of pages23
JournalTransactions in GIS
Volume25
Issue number3
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • General Earth and Planetary Sciences

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