AI 中文总结
针对现实世界大规模拼车系统订单分配和行程捆绑的核心挑战，提出开源标准化接口RideGym，解耦环境与算法，支持高效模拟，验证显示其高效，还揭示探索噪声对多智能体强化学习解决方案的影响。
AI 中文摘要
拼车已成为现代城市交通的重要组成部分，在多学科领域备受关注。其核心挑战是订单分配和行程捆绑，影响城市交通效率与碳排放。现有模拟平台缺乏标准化接口，多数研究者需从头构建定制环境。为此，我们提出RideGym，首个针对现实世界拼车系统中基于多智能体强化学习的订单调度的开源标准化Gym风格接口。它完全解耦环境与调度算法，支持在真实道路网络上进行高效大规模城市级模拟，验证实验显示其高效性，还揭示了探索噪声对多智能体强化学习解决方案性能和相对排名有显著影响。
英文摘要
Ride-sharing has become an essential component of modern urban transportation and has attracted significant attention across computer science, transportation, and management science. While the field spans a broad range of problems, such as driver relocation, dynamic pricing, and vehicle charging or fueling dispatch, the core challenge remains order assignment and trip bundling, which directly affect urban traffic efficiency and carbon emissions. Despite its importance, existing simulation platforms are typically tailored to specific operational studies or tightly coupled to a particular dispatch algorithm, and rarely expose a standardized, learning-friendly interface. As a result, most researchers still build customized environments from scratch, raising serious concerns about reproducibility and fair comparison, and incurring substantial redundant effort. To address this gap, we present RideGym, the first open-source, standardized Gym-style interface tailored to MARL-based order dispatch in real-world ride-sharing systems. By fully decoupling the environment from the dispatch algorithm, RideGym enables diverse learning-based and model-based methods to be developed and compared under identical, fully specified conditions. It supports efficient, large-scale city-level simulations on real road networks, and offers flexible configurations for vehicle attributes, order specifications, and automatic shortest-path routing. We validate RideGym by reproducing several baselines, and demonstrate its high efficiency, with a one-hour simulation involving thousands of vehicles and tens of thousands of orders completed within one minute across all methods. Moreover, we reveal that the choice of exploration noise can significantly affect both the performance and the relative ranking of MARL solutions, an aspect often overlooked in prior work.
