网约车已成为现代城市交通的重要组成部分，并在计算机科学、交通运输学与管理科学等领域受到广泛关注。该领域涵盖诸多问题，例如司机重定位、动态定价以及车辆充电或加油调度等，但其核心挑战仍为订单分配与行程拼单，这直接影响城市交通效率与碳排放。尽管该问题至关重要，现有仿真平台通常针对特定运营研究定制，或与某一派单算法深度耦合，极少提供标准化、利于学习的接口。因此，多数研究人员仍需从零构建定制化环境，严重损害了研究的可复现性与公平比较，同时造成大量重复性工作。为填补这一空白，我们提出 RideGym——首个面向基于多智能体强化学习（MARL）的订单派发、专为真实网约车系统设计的开源标准化 Gym 风格接口。RideGym 将环境与派单算法完全解耦，使各类基于学习的方法与基于模型的方法均可在相同且完全明确的条件下开发与对比。它支持基于真实路网的城市级高效大规模仿真，并提供车辆属性、订单规格及自动最短路径路由等灵活配置选项。我们通过复现多个基线方法验证了 RideGym 的有效性，并证明其具有高效率：在所有方法下，涉及数千辆车辆与数万订单的一小时仿真可在一分钟内完成。此外，我们发现探索噪声的选择会显著影响算法性能及其相对排序。

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.