Assessment of an Agent’s Wayfinding of the Urban Environment Through Reinforcement Learning

Authors : Mahad Imhemed, Can Uzun

Pages : 259-278

Doi:10.53710/jcode.1512798

View : 135 | Download : 70

Publication Date : 2024-09-30

Article Type : Research

Abstract :This simulation study explores wayfinding motivated behavioral patterns in the city through agent-based modelling. Agents were trained using Unity’s ML-Agents toolkit with reinforcement learning. The study uses the Sultan Ahmet Mosque and its surrounding boundary as a model environment for the training of an agent’s wayfinding. Agents are trained to locate the Sultan Ahmet Mosque target. The behaviors of agents trained with two different methods, “Complex” and “Simple” learning, comparing their navigation quests at various difficulty levels featuring respawn points. After the training of the agents, the alternative routes produced while attaining the target during the wayfinding process were analyzed. As an outcome of the analysis, it was observed that the agents were prone to go off-route, navigate to different locations they perceived in the urban space, and then would reach the target. This occurrence is justified as an agent’s curiosity trained through reinforcement learning. This study differs from the literature in a way that it attempts to understand the navigational behavior of agents that were trained with reinforcement learning. Moreover, this research discusses the perception of wayfinding through curiosity and aims to make a comprehension of the perception of the city, which is one of the key ideas in neurourbanism. The study contributes to the literature by showing that wayfinding behaviors acquired from agents’ curiosity-driven explorations and past experiences can be an input for neurourbanism, supporting urban design. It informs urban enhancements that are user-centric and rich in urban perception using the reinforcement learning method.
Keywords : Davranışsal Kalıplar, Motivasyon Merakı, nöroşehircilik, pekiştirmeli öğrenme, Kentsel Tasarım.