  We present the single track road problem. In this problem two agents face
each-other at opposite positions of a road that can only have one agent pass at
a time. We focus on the scenario in which one agent is human, while the other
is an autonomous agent. We run experiments with human subjects in a simple grid
domain, which simulates the single track road problem. We show that when data
is limited, building an accurate human model is very challenging, and that a
reinforcement learning agent, which is based on this data, does not perform
well in practice. However, we show that an agent that tries to maximize a
linear combination of the human's utility and its own utility, achieves a high
score, and significantly outperforms other baselines, including an agent that
tries to maximize only its own utility.
