扩展共享电动出行系统的车队再平衡：一种多智能体深度强化学习方法
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
AI总结：
针对扩展中的共享电动出行系统，提出一种考虑续航与充电约束的多智能体深度强化学习车队再平衡方法，通过高保真模拟与动作级联策略优化，显著提升需求满足率和净收入。
摘要：
共享出行的电气化已在全球范围内变得流行。许多城市已部署了新的共享电动出行系统，其覆盖范围从中心区域持续扩展到城市边缘。这些系统运营中的一个关键挑战是车队再平衡，即如何重新定位电动汽车以更好地满足未来需求。在系统扩展的背景下，这一问题尤其具有挑战性，因为：i) 电动汽车的续航里程有限，而充电时间通常较长，这限制了可行的再平衡操作；ii) 系统中的电动汽车站点是动态变化的，即再平衡操作的合法目标可能随时间变化。我们通过首先研究从真实世界共享电动出行系统收集的一年丰富数据集来应对这些挑战，分析这种新型出行模式的运营模型、使用模式和扩展动态。基于所学知识，我们设计了一个高保真模拟器，能够在细粒度上抽象电动汽车共享的关键运营细节。然后，我们将持续扩展下的共享电动出行系统的再平衡任务建模为一个多智能体强化学习（MARL）问题，该问题直接考虑了电动汽车的续航里程和充电特性。我们进一步提出了一种新颖的具有动作级联的策略优化方法，该方法能够应对扩展动态并解决所构建的MARL问题。我们对所提出的方法进行了广泛评估，实验结果表明，我们的方法优于现有最先进方法，在满足需求和净收入方面均提供了显著的性能提升。
英文摘要：
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.
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