Learning to Calibrate Hybrid Hyperparameters: a Study on Traffic Simulation

Abstract

Traffic simulation is an important computational technique that models the behavior and interactions of vehicles, pedestrians, and infrastructure in a transportation system. Calibration, which involves adjusting simulation parameters to match real-world data, is a key challenge in traffic simulation. Traffic simulators involve multiple models with hybrid hyperparameters, which could be either categorical or continuous. In this paper, we present CHy2, an approach that generates a set of hyperparameters for simulator calibration using generative adversarial imitation learning. CHy2 learns to mimic expert behavior models by rewarding hyperparameters that deceive a discriminator trained to classify policy-generated and expert trajectories. Specifically, we propose a hybrid architecture of actor-critic algorithms to handle the hybrid choices between hyperparameters. Experimental results show that CHy2 outperforms previous methods in calibrating traffic simulators.