  Shared-use autonomous mobility services (SAMS) present new opportunities for
improving accessible and demand-responsive mobility. A fundamental challenge
that SAMS face is appropriate positioning of idle fleet vehicles to meet future
demand - a problem that strongly impacts service quality and efficiency. This
paper formulates SAMS fleet repositioning as a Markov Decision Process and
presents a reinforcement learning-based repositioning (RLR) approach called
integrated system-agent repositioning (ISR). The ISR learns a scalable fleet
repositioning strategy in an integrated manner: learning to respond to evolving
demand patterns without explicit demand forecasting and to cooperate with
optimization-based passenger-to-vehicle assignment. Numerical experiments are
conducted using New York City taxi data and an agent-based simulation tool. The
ISR is compared to an alternative RLR approach named externally guided
repositioning (EGR) and a benchmark joint optimization (JO) for
passenger-to-vehicle assignment and repositioning. The results demonstrate the
RLR approaches' substantial reductions in passenger wait times, over 50%,
relative to the JO approach. The ISR's ability to bypass demand forecasting is
also demonstrated as it maintains comparable performance to EGR in terms of
average metrics. The results also demonstrate the model's transferability to
evolving conditions, including unseen demand patterns, extended operational
periods, and changes in the assignment strategy.
