约束多智能体强化学习管理电动自动驾驶出租车

Zhaoxing Yang, Guiyun Fan, Haiming Jin
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引用次数: 0

摘要

电动自动驾驶出租车(es-taxi)因其便利和环保的特点,备受关注,成为未来交通的希望。然而,有效管理大型电动出租车仍然是一个悬而未决的问题。本文主要研究收费预算约束下的电动出租车调度问题。具体地说,我们设计了安全控制器以保证预算约束的满足,并提出了HAT框架以扩大停用出租车决策的视野。对于HAT引起的非平稳性,我们用理论保证来分析和限制其影响。整体框架Safe-HAT在实际数据中与其他强基线相比实现了卓越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constrained Multi-Agent Reinforcement Learning for Managing Electric Self-Driving Taxis
Electric self-driving taxis (es-taxis) draw great attention nowadays and hold the promise for future transportation due to their convenient and environment-friendly nature. However efficiently managing large-scale es-taxis remains an open problem. In this paper, we focus on scheduling es-taxis under charging budget constraint. Specifically, we design safe-controller to guarantee the satisfaction of budget constraint, and propose HAT framework to enlarge the sight for decision-making on deactivating es-taxis. As for the non-stationary induced by HAT, we analyze and limit its influence with theoretical guarantees. The overall framework Safe-HAT achieves superior performance in real-world data against other strong baselines.
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