OpenHail: An Event-Driven Gymnasium Environment for Electric Ride-Hailing Fleet Control

2026-09-24

AI Summary

This study addresses the lack of standardized reinforcement learning environments for the joint control of electric vehicle fleets by developing an open-source simulation platform based on Gymnasium. Methodologically, it introduces a configurable decision epoch mechanism that unifies dispatching, scheduling, and charging decisions while accommodating diverse control paradigms. Furthermore, an event-driven architecture is adopted, integrating battery dynamics modeling, first-in-first-out charging queues, and a fixed interface design to achieve effective decoupling of internal events. This work provides seeded instances, evaluation tools, benchmark policies, and a complete codebase, thereby establishing a standardized experimental benchmark for research on the collaborative optimization of electric vehicle fleets.

Abstract

Machine-learning policies have attracted increasing interest for ride-hailing fleet control in recent years. Reinforcement learning, in particular, requires a structured simulation environment that specifies observations, actions, rewards, and decision epochs for training and evaluation. For electric fleets, this environment must also capture the interaction among stochastic demand, vehicle operations, and capacitated charging infrastructure. We present OpenHail, an open-source Gymnasium environment for joint control of electric ride-hailing fleets. Its fixed-size observation--action interface exposes request assignment, repositioning, and charging to a single policy. The event-driven simulator represents requests with pickup deadlines, vehicle job queues, battery dynamics, and finite-capacity charging facilities with first-in--first-out queues. A configurable decision-epoch mechanism separates internal simulator events from policy interactions, supporting event-driven, periodic, hybrid, and policy-requested control within the same operational model. The software provides seeded instances, feasible-action utilities, evaluation tools, operational metrics, and baseline policies. The source code is available at https://github.com/tommaso-schettini/openhail.

Problem

• Electric ride-hailing
• Fleet control
• Reinforcement learning
• Simulation environment
• Charging infrastructure

Innovation

• Event-driven simulation
• Electric ride-hailing
• Reinforcement learning
• Gymnasium environment
• Fleet control