Energy-Saving Train Operation Synergy Based on Multi-Agent Deep Reinforcement Learning on Spark Cloud-Reference-Cited by-同舟云学术

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Energy-Saving Train Operation Synergy Based on Multi-Agent Deep Reinforcement Learning on Spark Cloud

Published:2023-01 Issue:1 Volume:72 Page:214-226
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Shang Mengying¹^ORCID,Zhou Yonghua¹^ORCID,Mei Yiduo²,Zhao Jie³,Fujita Hamido⁴^ORCID

Affiliation:

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

2. Zhongguancun Smart City Company, Ltd, Beijing, China

3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

4. Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam

Funder

Beijing Natural Science Foundation

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10017140/09882357.pdf?arnumber=9882357

Reference31 articles.

1. Learning action representations for reinforcement learning;chandak;Proc Int Conf Mach Learn,2019

2. Deep reinforcement learning with reference system to handle constraints for energy-efficient train control

3. Multi-train trajectory optimization for energy-efficient timetabling

4. Joint optimization of high-speed train timetables and speed profiles: A unified modeling approach using space-time-speed grid networks

5. Design of energy-Efficient timetables in two-way railway rapid transit lines

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2. Integrated Study of Train Scheduling and Control Considering Dynamic Passenger Flow and Energy Saving;2023

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