RLCharge: Imitative Multi-Agent Spatiotemporal Reinforcement Learning for Electric Vehicle Charging Station Recommendation-Reference-Cited by-同舟云学术

RLCharge: Imitative Multi-Agent Spatiotemporal Reinforcement Learning for Electric Vehicle Charging Station Recommendation

Published:2022 Issue: Volume: Page:1-1
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Zhang Weijia¹,Liu Hao²,Xiong Hui³,Xu Tong⁴,Wang Fan⁵,Xin Haoran⁴,Wu Hua⁵

Affiliation:

1. Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

2. Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China and the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China

3. HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, China, the Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China, and the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China

4. University of Science and Technology of China, Hefei, China

5. Baidu Inc., Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/4358933/09784819.pdf?arnumber=9784819

Reference57 articles.

1. Toward Pre-Empted EV Charging Recommendation Through V2V-Based Reservation System