In this paper we present a method based on the "learning from demonstration" paradigm to get a cost-efficient control policy in a continuous state and action space. The controlled plant is a two degrees-of-freedom planar arm actuated by six muscles. We learn a parametric control policy with xcsf from a few near-optimal trajectories, and we study its capability to generalize over the whole reachable space. Furthermore, we show that an additional Cross-Entropy Policy Search method can improve the global performance of the parametric controller.