Feedback error learning (FEL) is a classical computational model that describes human motor learning. It consists of forward and inverse models representing internal dynamics and environmental disturbances. Such models can be used as controllers that represent the function of the motor cortex. On top of FEL, a model has been built with jointly trained feedforward and feedback controllers using a neural network. The controllers that actuated six muscles driving a two-degrees-of-freedom arm model were trained offline. This model successfully simulated human learning of point-to-point reaching movements in a horizontal plane when it was tasked to adapt itself in a null field (NF) and a velocity-dependent force field (VF). In this study, we further tested this model in the divergent force fields (DF) and a channelled force field (CF) to observe its performance. The comparison between the simulation results and the experimental evidence suggests that this model can predict some of the key features of the learning process, such as the kinematics, the muscle dynamics, and the impedance profiles. The learning decay was hindered when the lateral error was artificially eliminated by the CF, as reported in the literature. Overall, the model could converge towards realistic