arXiv cs.AI· Jim Dai, Manxi Wu, Zhanhao Zhang·· 2 天前AI 评分22
Atomic-PPO：面向电动 Robo-Taxi 调度与充电桩分配的原子近端策略优化
Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation
AI 导读
研究提出 Atomic-PPO 深度强化学习算法，通过原子动作分解缩减动作空间，以较低算力开销求解电动 Robo-Taxi 的车队调度与充电桩分配问题。基于纽约市真实网约车出行记录标定的仿真显示，该算法能产出接近最优的调度策略，并可作为系统评估工具。实验还表明，充电桩布局应贴合乘客需求分布，充电桩数量与充电速度需均衡投入，且充电基础设施应与车队规模协同规划。
正文 · 原文
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 environment while accounting for limited charging infrastructure. Understanding both how to design efficient, scalable control algorithms for large fleets and how charger deployment shapes system performance is therefore critical for robo-taxi operators. We model the operations of an electric robo-taxi system as a discrete-time Markov decision process with a long-run average reward objective. To efficiently evaluate system performance under different configurations, we develop a scalable deep reinforcement learning algorithm, Atomic Proximal Policy Optimization (Atomic-PPO), which reduces the action space using atomic action decomposition. The resulting training procedure requires only modest computational resources, making it practical to evaluate a large number of system configurations. We calibrate a simulation environment using real-world NYC for-hire vehicle trip records and benchmark Atomic-PPO against heuristic policies and a fluid-based reward upper bound. The results show that Atomic-PPO produces near-optimal fleet dispatching policies and serves as a reliable tool for system evaluation. We conduct extensive numerical experiments to evaluate how charging infrastructure and fleet size affect system performance. Our findings highlight the importance of ridership-based charger placement, balanced investments in charger quantity and charging speed, and joint planning of charging infrastructure and fleet size.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2502.13392 [cs.AI] |
| (or arXiv:2502.13392v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2502.13392 arXiv-issued DOI via DataCite |
Submission history
From: Zhanhao Zhang [view email]
[v1]
Wed, 19 Feb 2025 03:05:23 UTC (11,728 KB)
[v2]
Sun, 27 Apr 2025 17:05:51 UTC (11,739 KB)
[v3]
Thu, 15 Oct 2026 04:21:47 UTC (834 KB)
来源：arXiv cs.AI · arxiv.org