RideGym: Standardized MARL Interface for 1-Minute City Ride-Sharing Simulation
New open-source platform simulates an hour of ride-sharing across thousands of vehicles in under a minute.
Ride-sharing is a critical component of urban transportation, yet research progress has been hampered by fragmented simulation platforms. Most existing tools are tailored to specific algorithms or operational studies, lacking a standardized, learning-friendly interface. This forces researchers to build custom environments from scratch, leading to reproducibility issues and redundant effort. To address this, a team from an undisclosed institution (authors Zhao, Hu, Li) has released RideGym—an open-source, Gym-style interface designed specifically for MARL-based order dispatch in ride-sharing systems.
RideGym fully decouples the environment from the dispatch algorithm, allowing diverse learning-based and model-based methods to be developed and compared under identical 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. In benchmarks, a one-hour simulation involving thousands of vehicles and tens of thousands of orders completed within one minute across all tested methods, demonstrating high efficiency. Additionally, the researchers reveal a crucial finding: the choice of exploration noise can significantly impact both the performance and relative ranking of MARL solutions—a factor often overlooked in prior work. This insight underscores the need for standardized benchmarks to ensure fair comparisons in ride-sharing research.
- RideGym is the first open-source, standardized Gym-style interface for MARL order dispatch in real-world ride-sharing systems.
- A one-hour city-scale simulation with thousands of vehicles and tens of thousands of orders runs in under one minute.
- The paper reveals that exploration noise choice significantly affects MARL performance and ranking, often ignored in previous studies.
Why It Matters
Standardized simulation enables reproducible ride-sharing AI research, accelerating progress toward efficient urban mobility and reduced emissions.