  We view intersection handling on autonomous vehicles as a reinforcement
learning problem, and study its behavior in a transfer learning setting. We
show that a network trained on one type of intersection generally is not able
to generalize to other intersections. However, a network that is pre-trained on
one intersection and fine-tuned on another performs better on the new task
compared to training in isolation. This network also retains knowledge of the
prior task, even though some forgetting occurs. Finally, we show that the
benefits of fine-tuning hold when transferring simulated intersection handling
knowledge to a real autonomous vehicle.
