Multi-Agent Reinforcement Learning-Based Trading Decision-Making in Platooning-Assisted Vehicular Networks

Author:

Xiao Tingting1ORCID,Chen Chen1ORCID,Dong Mianxiong2ORCID,Ota Kaoru2ORCID,Liu Lei1ORCID,Dustdar Schahram3ORCID

Affiliation:

1. School of Telecommunications Engineering, Xidian University, Xi’an, China

2. Department of Information and Electronic Engineering, Muroran Institute of Technology, Muroran, Japan

3. Distributed Systems Group, TU Wien, Vienna, Austria

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Research and Development Plan of Shaanxi Province

Natural Science Foundation of Guangdong Province of China

Key Project on Artificial Intelligence of Xi’an Science and Technology Plan

Xi’an Science and Technology Plan

Proof-of-Concept Fund from the Hangzhou Research Institute of Xidian University

Japan Society for the Promotion of Science (JSPS) KAKENHI

Leading Initiative for Excellent Young Researchers (LEADER), the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan

Japan Science and Technology Agency (JST), Precursory Research for Embryonic Science and Technology (PRESTO), Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Software

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