The electrification of shared mobility has become popular across the globe.
Many cities have their new shared e-mobility systems deployed, with
continuously expanding coverage from central areas to the city edges. A key
challenge in the operation of these systems is fleet rebalancing, i.e., how EVs
should be repositioned to better satisfy future demand. This is particularly
challenging in the context of expanding systems, because i) the range of the
EVs is limited while charging time is typically long, which constrain the
viable rebalancing operations; and ii) the EV stations in the system are
dynamically changing, i.e., the legitimate targets for rebalancing operations
can vary over time. We tackle these challenges by first investigating rich sets
of data collected from a real-world shared e-mobility system for one year,
analyzing the operation model, usage patterns and expansion dynamics of this
new mobility mode. With the learned knowledge we design a high-fidelity
simulator, which is able to abstract key operation details of EV sharing at
fine granularity. Then we model the rebalancing task for shared e-mobility
systems under continuous expansion as a Multi-Agent Reinforcement Learning
(MARL) problem, which directly takes the range and charging properties of the
EVs into account. We further propose a novel policy optimization approach with
action cascading, which is able to cope with the expansion dynamics and solve
the formulated MARL. We evaluate the proposed approach extensively, and
experimental results show that our approach outperforms the state-of-the-art,
offering significant performance gain in both satisfied demand and net revenue.

本文分析了真实的共享电动出行系统的运营模式、使用模式及扩张动态，并以此为基础，利用多智能体强化学习建立了一个高保真度的电动出行共享仿真器，在此基础上提出了一种新的基于动态优化的级联策略的解法，显著提高了系统对于未来需求的满足度和增加收益。