[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85834-en":3,"doc-seo-85834-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85834,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","RideGym: A Standardized Interface for Real-World Large-Scale Ride-Sharing System","Ride-sharing has become essential to modern urban transportation, with research focus spanning driver relocation, dynamic pricing, and vehicle charging or fueling dispatch. Across these topics, order assignment and trip bundling remain the central lever shaping traffic efficiency and carbon emissions. Existing simulators often target narrow studies or specific dispatch algorithms, limiting reproducibility and fair comparison. RideGym introduces an open-source, standardized Gym-style interface for MARL-based order dispatch, decoupling environment and policy for controlled learning.","RideGym: A Standardized Interface for Real-World Large-Scale Ride-Sharing System  \nZijian Zhao  \nThe Hong Kong University of Science and Technology Hong Kong, China [zzhaock@connect.ust.hk](zzhaock@connect.ust.hk)  \nYulong Hu  \nThe Hong Kong University of Science and Technology Hong Kong, China [yhucm@connect.ust.hk](yhucm@connect.ust.hk)  \nSen Li*  \nThe Hong Kong University of Science and Technology Hong Kong, China[cesli@ust.hk](cesli@ust.hk)  \narXiv :2607 . 10173v1 [ cs .MA] 11 Jul 2026  \nAbstract  \nRide-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 opensource, standardized Gym-style interface tailored to Multi-Agent Reinforcement Learning (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 (e.g., personalized speeds and capacities), order specifications (e.g., multiple passengers per order), 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. Our code is available at [https://github.com/RS2002/RideGym](https://github.com/RS2002/RideGym), and the Python package can be installed via pip install ride-gym.  \nCCS Concepts  \n• Applied computing → Transportation; • Computing methodologies → Multi-agent planning.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nConference acronym ’XX, Woodstock, NY  \n© 2018 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-1-4503-XXXX-X/2018/06  \n[https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \nKeywords  \nRide Sharing, Simulation Gym, Standardized Interface, Multi-Agent Reinforcement Learning (MARL)  \nACM Reference Format:  \nZijian Zhao, Yulong Hu, and Sen Li* . 2018. RideGym: A Standardized Interface for Real-World Large-Scale Ride-Sharing System. In Proceedings of Make sure to enter the correct conference title from your rights confirmation email (Conf","cbCainK8Lfq0Ex10","https://ap.wps.com/l/cbCainK8Lfq0Ex10","pdf",1937983,3,1,12,"English","en",105,"# Introduction\n## Core role of order assignment and bundling\n## Challenges in ride-sharing order dispatch\n## RideGym interface overview","[{\"question\":\"What problem does RideGym target in real-world ride-sharing research?\",\"answer\":\"It targets order assignment and trip bundling for large-scale ride-sharing, providing a standardized simulation interface for MARL-based dispatch.\"},{\"question\":\"How does RideGym improve reproducibility and fair comparisons?\",\"answer\":\"By decoupling the environment from the dispatch algorithm, RideGym enables different learning methods to run under identical, fully specified conditions.\"},{\"question\":\"What capabilities does RideGym provide for simulation at city scale?\",\"answer\":\"It supports efficient city-level simulations on real road networks, flexible configuration of vehicle attributes and order specifications, and automatic shortest-path 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