Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning

TL;DR

This paper introduces the use of Inverse Reinforcement Learning to create realistic agents for participatory urban simulation, enhancing modeling of human behavior in urban planning contexts.

Contribution

It applies IRL to urban simulation to better generate agents that reflect social phenomena, addressing limitations of previous models.

Findings

IRL enables more realistic agent behavior modeling.

The approach highlights new possibilities for participatory urban simulation.

Challenges of IRL in this context are discussed.

Abstract

The multiagent-based participatory simulation features prominently in urban planning as the acquired model is considered as the hybrid system of the domain and the local knowledge. However, the key problem of generating realistic agents for particular social phenomena invariably remains. The existing models have attempted to dictate the factors involving human behavior, which appeared to be intractable. In this paper, Inverse Reinforcement Learning (IRL) is introduced to address this problem. IRL is developed for computational modeling of human behavior and has achieved great successes in robotics, psychology and machine learning. The possibilities presented by this new style of modeling are drawn out as conclusions, and the relative challenges with this modeling are highlighted.