Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

EvacuAI: An analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning

Anna Carolina Rosa
* ,
Mariana Cabral Falqueiro
,
Rodrigo Bonacin
ORCID logo ,
Fábio Lúcio Lopes De Mendonça
ORCID logo ,
Geraldo Pereira Rocha Filho
,
Vinícius Pereira Gonçalves
Version 1 : Received: 30 June 2023 / Approved: 4 July 2023 / Online: 4 July 2023 (10:38:05 CEST)

A peer-reviewed article of this Preprint also exists.

Rosa, A.C.; Falqueiro, M.C.; Bonacin, R.; de Mendonça, F.L.L.; Filho, G.P.R.; Gonçalves, V.P. EvacuAI: An Analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning. Sensors 2023, 23, 8892. Rosa, A.C.; Falqueiro, M.C.; Bonacin, R.; de Mendonça, F.L.L.; Filho, G.P.R.; Gonçalves, V.P. EvacuAI: An Analysis of Escape Routes in Indoor Environments with the Aid of Reinforcement Learning. Sensors 2023, 23, 8892.

Abstract

There is only a very short reaction time for people to find the best way out of a building in a fire outbreak. Software applications can be used to assist the rapid evacuation of victims; however, this is an arduous task, which requires an understanding of advanced technologies. Since well-known pathway algorithms (such as, Dijkstra, Bellman-Ford and A*) can lead to serious problems over performance, we decided to make use of deep reinforcement learning techniques. A wide range of strategies including a random initialization of replay buffer and transfer learning were assessed in three projects involving schools of different sizes. The results showed the proposal was viable and that in most cases the performance of transfer learning was superior. In addition, the study raised challenges that had to be faced in the future.

Keywords

machine learning; deep reinforcement learning; transfer learning; fire; evacuation

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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