Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning
This addresses urban planning challenges by potentially improving simulation realism, but it appears incremental as it applies an existing IRL method to a new domain without demonstrated gains.
The paper tackles the problem of generating realistic agents for multiagent-based participatory urban simulations by introducing Inverse Reinforcement Learning (IRL) to model human behavior, though it does not report specific numerical results.
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.