Traffic simulation has gained a lot of interest for massive safety evaluation of self-driving systems in a risk free setting but the reality gap remains a big challenge. Adversarial Imitation learning (AIL) already proved that it is possible to learn driving policies from real demonstrations and more specifically on Highways (NGSIM dataset). However traffic interactions remains very restricted on those scenarios and it is necessary to cope with various and multiple real traffic participants to get real insight of human driving style. INTERACTION dataset was specifically designed for those concerns with complex and rich interactions on a variety of scenes like intersections, roundabouts, ramp merging in different countries. In this paper we introduce our training pipeline that is built upon the Lanelet2 road map format for learning human like driving policies based on most recent implementations of Adversarial Imitation Learning (AIL) algorithms. We compare different AIL algorithms and Behavioural Cloning (BC) baseline on various driving scenes and investigate how realistic driving policies can become as well as their ability to generalise on new scenes. We show that driving policies not only follow expert trajectories but also get safer with less offroad driving and collisions than BC baseline. This work opens new possibilities for multi agent traffic learning based on AIL techniques with real and highly interactive traffic data.