Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models-Reference-Cited by-同舟云学术

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Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models

Published:2024-08 Issue:8 Volume:73 Page:12110-12114
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Lin Hongyi¹^ORCID,Lyu Cheng²^ORCID,He Yixu³^ORCID,Liu Yang¹^ORCID,Gao Kun⁴^ORCID,Qu Xiaobo¹^ORCID

Affiliation:

1. School of Vehicle and Mobility, Tsinghua University, Beijing, China

2. Department of Mobility Systems Engineering, School of Engineering and Design, Technical University of Munich, Munich, Germany

3. School of Information Engineering, Chang'an University, Xi'an, China

4. Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden

Funder

National Natural Science Foundation of China

State Key Laboratory of Intelligent Green Vehicle and Mobility

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/25/10637911/10463154.pdf?arnumber=10463154

Reference32 articles.

1. Research progress on key technologies in the cooperative vehicle Infrastructure system;Lin;J. South China Univ. Technol. (Natural Sci. Ed.),2023

2. Envisioning the future of transportation: Inspiration of ChatGPT and large models

3. Can language models be used for real-world urban-delivery route optimization?

4. How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey

5. Future Role of Artificial Intelligence in Advancing Transportation Electrification

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1. Entire route eco-driving method for electric bus based on rule-based reinforcement learning;Transportation Research Part E: Logistics and Transportation Review;2024-09

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