  This paper studies the impact of the initial data gathering method on the
subsequent learning of a dynamics model. Dynamics models approximate the true
transition function of a given task, in order to perform policy search directly
on the model rather than on the costly real system. This study aims to
determine how to bootstrap a model as efficiently as possible, by comparing
initialization methods employed in two different policy search frameworks in
the literature. The study focuses on the model performance under the
episode-based framework of Evolutionary methods using probabilistic ensembles.
Experimental results show that various task-dependant factors can be
detrimental to each method, suggesting to explore hybrid approaches.
