  Taxis spend a significant amount of time idle, searching for passengers. The
routes vacant taxis should follow in order to minimize their idle times are
hard to calculate; they depend on complex quantities like passenger demand,
traffic conditions, and inter-taxi competition. Here we explore if
reinforcement learning (RL) can be used for this purpose. Using real-world data
to characterize passenger demand, we show RL-taxis indeed learn to how to
reduce their idle time in many environments. In particular, a single RL-taxi
operating in a population of regular taxis learns to out-perform its rivals by
a significant margin.
