Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES

Abstract

The Digital Center (DC) building at UNNES is a new building on the campus that currently lacks evacuation routes. Therefore, it is necessary to create an evacuation route plan in accordance with the Minister of Health Regulation Number 48 of 2016. Creating a manual evacuation route plan can be inefficient and prone to errors, especially for large buildings with complex interiors. To address this issue, learning techniques such as reinforcement learning (RL) are being used. In this study, Q-learning will be utilized to find the shortest path to the exit doors from 11 rooms on the first floor of the DC building. The study makes use of the floor plan data of the DC building, information about the location of the exit doors, and the distance required to reach them. The results of the study demonstrate that Qlearning successfully identifies the shortest evacuation routes for the first-floor DC building. The routes generated by Q-learning are identical to the manually created shortest paths. Even when additional obstacles are introduced into the environment, Q-learning is still able to find the shortest routes. On average, the number of episodes required for convergence in both environments is less than 1000 episodes, and the average computation time needed for both environments is 0.54 seconds and 0.76 seconds, respectively.