Escape Route Strategies in Complex Emergency Situations using Deep Reinforcement Learning

Abstract

In this work, we have developed a novel intelligent system capable of detecting and managing dynamic hazards in intelligent buildings. Our calculation of escape route strategies, numerical analysis, and visualization of evacuations, makes it possible to realistically investigate and evaluate hazards. For this purpose, we translated a real building into a static 3D model based on a building plan. For the analysis of evacuation scenarios, dynamic hazards were developed, which can also propagate dynamically over time. The computation of the escape route strategies is performed by using the Deep Reinforcement Learning (DRL) method Proximal Policy optimization (PPO). This work demonstrates that dynamic hazards have a great impact on the evacuation strategy in the building and can be analyzed by using this approach. Compared to traditional AI frameworks, scenarios can be created and analyzed both numerically and visually. As a result, the behavior of agents during training and evacuation can be examined for natural behavior.