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

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 people from the building; 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 performance problems, when it comes to multi-objective problems, 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, enabling the learning agent to be trained in times shorter than 1 min, with 100% accuracy in the routes. In addition, the study raised challenges that had to be faced in the future.