Unal, Ahmet EminGezer, CengizKuleli Pak, Burcu KuleliGüngör, Vehbi Çağrı2025-09-252025-09-2520229781665488945https://doi.org/10.1109/ASYU56188.2022.9925560https://hdl.handle.net/20.500.12573/3892In an emergency, it is vital to evacuate individuals from the dangerous environments. Emergency evacuation plan-ning ensures that the evacuation is safe and optimal in terms of evacuation time for all of the people in evacuation. To this end, the computer-enabled evacuation simulation systems are used to generate optimal routes for the evacuees. In this paper, a dynamic emergency evacuation route generator has been proposed based on indoor plans of the building and the locations of the evacuees. To generate the optimal routes in real-time, a reinforcement learning algorithm (proximal policy optimization) is presented. Comparative performance results show that the proposed model is successful for evacuating the individuals from the building in different scenarios. © 2022 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessCrowd SimulationDeep LearningEmergency EvacuationPath PlanningReinforcement LearningDeep LearningLearning AlgorithmsLearning SystemsReinforcement LearningCrowd SimulationEmergency EvacuationEvacuation PlansEvacuation RoutesEvacuation Simulation SystemEvacuation TimeOptimal RoutesReinforcement LearningsRoute DirectionsMotion PlanningConference Object10.1109/ASYU56188.2022.99255602-s2.0-85142725214