  New forms of on-demand transportation such as ride-hailing and connected
autonomous vehicles are proliferating, yet are a challenging use case for
electric vehicles (EV). This paper explores the feasibility of using deep
reinforcement learning (DRL) to optimize a driving and charging policy for a
ride-hailing EV agent, with the goal of reducing costs and emissions while
increasing transportation service provided. We introduce a data-driven
simulation of a ride-hailing EV agent that provides transportation service and
charges energy at congested charging infrastructure. We then formulate a test
case for the sequential driving and charging decision making problem of the
agent and apply DRL to optimize the agent's decision making policy. We evaluate
the performance against hand-written policies and show that our agent learns to
act competitively without any prior knowledge.
