  Traffic simulation has gained a lot of interest for quantitative evaluation
of self driving vehicles performance. In order for a simulator to be a valuable
test bench, it is required that the driving policy animating each traffic agent
in the scene acts as humans would do while maintaining minimal safety
guarantees. Learning the driving policies of traffic agents from recorded human
driving data or through reinforcement learning seems to be an attractive
solution for the generation of realistic and highly interactive traffic
situations in uncontrolled intersections or roundabouts. In this work, we show
that a trade-off exists between imitating human driving and maintaining safety
when learning driving policies. We do this by comparing how various Imitation
learning and Reinforcement learning algorithms perform when applied to the
driving task. We also propose a multi objective learning algorithm (MOPPO) that
improves both objectives together. We test our driving policies on highly
interactive driving scenarios extracted from INTERACTION Dataset to evaluate
how human-like they behave.
