World Model Learning from Demonstrations with Active Inference: Application to Driving Behavior

Active inference proposes a unifying principle for perception and action as jointly minimizing the free energy of an agent's internal world model. In the active inference literature, world models are typically pre-specified or learned through interacting with an environment. This paper explores the possibility of learning world models of active inference agents from recorded demonstrations, with an application to human driving behavior modeling. The results show that the presented method can create models that generate human-like driving behavior but the approach is sensitive to input features.