  Reactive and safe agent modelings are important for nowadays traffic
simulator designs and safe planning applications. In this work, we proposed a
reactive agent model which can ensure safety without comprising the original
purposes, by learning only high-level decisions from expert data and a
low-level decentralized controller guided by the jointly learned decentralized
barrier certificates. Empirical results show that our learned road user
simulation models can achieve a significant improvement in safety comparing to
state-of-the-art imitation learning and pure control-based methods, while being
similar to human agents by having smaller errors to the expert data. Moreover,
our learned reactive agents are shown to generalize better to unseen traffic
conditions, and react better to other road users and therefore can help
understand challenging planning problems pragmatically.
