  We analyze how the knowledge to autonomously handle one type of intersection,
represented as a Deep Q-Network, translates to other types of intersections
(tasks). We view intersection handling as a deep reinforcement learning
problem, which approximates the state action Q function as a deep neural
network. Using a traffic simulator, we show that directly copying a network
trained for one type of intersection to another type of intersection decreases
the success rate. We also show that when a network that is pre-trained on Task
A and then is fine-tuned on a Task B, the resulting network not only performs
better on the Task B than an network exclusively trained on Task A, but also
retained knowledge on the Task A. Finally, we examine a lifelong learning
setting, where we train a single network on five different types of
intersections sequentially and show that the resulting network exhibited
catastrophic forgetting of knowledge on previous tasks. This result suggests a
need for a long-term memory component to preserve knowledge.
