English(EN) Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation

新算法优化电动机器人出租车调度和充电

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一种名为原子近端策略优化（Atomic-PPO）的新型深度强化学习算法，以优化电动机器人出租车车队的运营。该算法分解了动作空间，使其在评估各种系统配置时具有计算效率。使用纽约市的租赁车辆数据进行测试时，Atomic-PPO 展现了近乎最优的车队调度策略，并深入分析了充电基础设施和车队规模对系统性能的影响。 AI

影响该算法可以提高自动驾驶出行共享服务的效率和成本效益。

排序理由这是一篇详细介绍特定应用新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jim Dai, Manxi Wu, Zhanhao Zhang · 2026-10-16 04:00

面向电动机器人出租车调度和充电器分配的原子近端策略优化

arXiv:2502.13392v3 Announce Type: replace Abstract: Pioneering companies such as Waymo have deployed electric robo-taxi services in several U.S. cities. Operating such fleets requires coordinating ride matching, vehicle repositioning, and battery charging in a stochastic environm…

本文如何被排名

报道来源 [1]

面向电动机器人出租车调度和充电器分配的原子近端策略优化

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