A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems-Reference-Cited by-同舟云学术

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A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
language:
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Author:

He Sihong¹,Wang Yue²,Han Shuo³,Zou Shaofeng²,Miao Fei¹

Affiliation:

1. University of Connecticut,Department of Computer Science and Engineering,Storrs,CT

2. University at Buffalo, The State University of New York,Department of Electrical Engineering

3. University of Illinois,Department of Electrical and Computer Engineering,Chicago

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10341341/10341342/10342342.pdf?arnumber=10342342

Reference40 articles.

1. Analysis and Control of Autonomous Mobility-on-Demand Systems

2. Rebalancing shared mobility-on-demand systems: A reinforcement learning approach

3. Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties

4. Data-driven Distributionally Robust Optimization For Vehicle Balancing of Mobility-on-Demand Systems

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Formal Modelling for Multi-Robot Systems Under Uncertainty;Current Robotics Reports;2023-08-15

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