Multiple-Agent Deep Reinforcement Learning for Avatar Migration in Vehicular Metaverses-Reference-Cited by-同舟云学术

Multiple-Agent Deep Reinforcement Learning for Avatar Migration in Vehicular Metaverses

Published:2023-04-30 Issue: Volume: Page:
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Container-title:Companion Proceedings of the ACM Web Conference 2023
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Author:

Chen Junlong¹^ORCID,Nie Jiangtian²^ORCID,Xu Minrui²^ORCID,Lyu Lingjuan³^ORCID,Xiong Zehui⁴^ORCID,Kang Jiawen¹^ORCID,Tong Yongju¹^ORCID,Jiang Wenchao⁴^ORCID

Affiliation:

1. Guangdong University of Technology, China

2. Nanyang Technological University, Singapore

3. Sony(Japan), Japan

4. Singapore University of Technology and Design, Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543873.3587573

Reference20 articles.

1. 6G-Enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions

2. Nam H Chu, Dinh Thai Hoang, Diep N Nguyen, Khoa T Phan, and Eryk Dutkiewicz. 2022. MetaSlicing: A Novel Resource Allocation Framework for Metaverse. arXiv preprint arXiv:2205.11087 (2022).

3. Joint Optimization of Handover Control and Power Allocation Based on Multi-Agent Deep Reinforcement Learning

4. Shibo He, Kun Shi, Chen Liu, Bicheng Guo, Jiming Chen, and Zhiguo Shi. 2022. Collaborative Sensing in Internet of Things: A Comprehensive Survey. IEEE Communications Surveys & Tutorials (2022).

5. Joint User Association and Resource Pricing for Metaverse: Distributed and Centralized Approaches

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4. Efficient approaches for task offloading in point-of-interest based vehicular fog computing;The Journal of Supercomputing;2023-10-21