Multi-Agent Imitation Learning for Driving Simulation
  Simulation is an appealing option for validating the safety of autonomous
vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been
shown to learn representative human driver models. These human driver models
were learned through training in single-agent environments, but they have
difficulty in generalizing to multi-agent driving scenarios. We argue these
difficulties arise because observations at training and test time are sampled
from different distributions. This difference makes such models unsuitable for
the simulation of driving scenes, where multiple agents must interact
realistically over long time horizons. We extend GAIL to address these
shortcomings through a parameter-sharing approach grounded in curriculum
learning. Compared with single-agent GAIL policies, policies generated by our
PS-GAIL method prove superior at interacting stably in a multi-agent setting
and capturing the emergent behavior of human drivers.
