One of the great abilities of the central nervous system (CNS) is that it can learn by itself how to control our body to execute required tasks. Although several motor control models have been proposed to explain well-learned arm reaching movements, those models do not fully consider how the CNS learns to control our body. In this paper, we propose a new motor control model that can learn to generate accurate reaching movements without prior knowledge of arm dynamics. In our model, the control law is learned in a trial-and-error manner using the reward signal. We focus on point-to-point arm reaching task in the sagittal plane and show that accurate reaching movements toward any given point can be learned and generated by our model. Furthermore, the model can predict human subjects' hand trajectories without specifying desired trajectories.