Many robotic projects use simulation as a faster and easier way to develop, evaluate and validate software components compared with on-board real world settings. In the human-robot interaction field, some recent works have attempted to integrate humans in the simulation loop. In this paper we investigate how such kind of robotic simulation software can be used to provide a dynamic and interactive environment to both collect a multimodal situated dialogue corpus and to perform an efficient reinforcement learning-based dialogue management optimisation procedure. Our proposition is illustrated by a preliminary experiment involving real users in a Pick-Place-Carry task for which encouraging results are obtained.