We evaluate the potential of Guided Policy Search (GPS), a model-based reinforcement learning (RL) method, to train a robot controller for human-robot object handovers. Handovers are a key competency for collaborative robots and GPS could be a promising approach for this task, as it is data efficient and does not require prior knowledge of the robot and environment dynamics. However, existing uses of GPS did not consider important aspects of human-robot handovers, namely large spatial variations in reach locations, moving targets, and generalizing over mass changes induced by the object being handed over. In this work, we formulate the reach phase of handovers as an RL problem and then train a collaborative robot arm in a simulation environment. Our results indicate that GPS is limited in the spatial generalizability over variations in the target location, but that this issue can be mitigated with the addition of local controllers trained over target locations in the high error regions. Moreover, learned policies generalize well over a large range of end-effector masses. Moving targets can be reached with comparable errors using a global policy trained on static targets, but this results in inefficient, high-torque, trajectories. Training on moving targets improves trajectories, but results in worse worst-case performance. Initial results suggest that lower-dimensional state representations are beneficial for GPS performance in handovers.