  Electric vehicles have been rapidly increasing in usage, but stations to
charge them have not always kept up with demand, so efficient routing of
vehicles to stations is critical to operating at maximum efficiency. Deciding
which stations to recommend drivers to is a complex problem with a multitude of
possible recommendations, volatile usage patterns and temporally extended
consequences of recommendations. Reinforcement learning offers a powerful
paradigm for solving sequential decision-making problems, but traditional
methods may struggle with sample efficiency due to the high number of possible
actions. By developing a model that allows complex representations of actions,
we improve outcomes for users of our system by over 30% when compared to
existing baselines in a simulation. If implemented widely, these better
recommendations can globally save over 4 million person-hours of waiting and
driving each year.
