OpenHail：用于电动网约车车队控制的事件驱动Gymnasium环境
OpenHail: An Event-Driven Gymnasium Environment for Electric Ride-Hailing Fleet Control
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中文总结 AI 辅助
OpenHail是一个开源Gymnasium环境，用于电动网约车车队联合控制，通过事件驱动仿真和可配置决策周期机制，支持请求分配、重新定位和充电的强化学习策略训练与评估。
中文摘要 AI 辅助
近年来，机器学习策略在网约车车队控制领域引起了越来越多的关注。特别是强化学习，需要一个结构化的仿真环境来指定观测、动作、奖励和决策周期，以进行训练和评估。对于电动车队，该环境还必须捕捉随机需求、车辆运营和有限容量充电基础设施之间的相互作用。我们提出了OpenHail，一个用于电动网约车车队联合控制的开源Gymnasium环境。其固定大小的观测-动作接口将请求分配、重新定位和充电暴露给单一策略。事件驱动仿真器以取件截止时间、车辆作业队列、电池动态以及具有先进先出队列的有限容量充电设施来表示请求。可配置的决策周期机制将内部仿真器事件与策略交互分离，支持在同一运营模型内进行事件驱动、周期、混合和策略请求的控制。该软件提供了种子实例、可行动作工具、评估工具、运营指标和基线策略。源代码可在该URL获取。
英文摘要
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.
发表机构
- Concordia University（康考迪亚大学）
- Université de Tours（图尔大学）
- HEC Montréal（蒙特利尔高等商学院）
机构由 AI 辅助整理，请以论文原文为准。
