Online parking assignment in an environment of partially connected vehicles: A multi-agent deep reinforcement learning approach

被引：0
|
作者：
Zhang, Xinyuan [1 ]
Zhao, Cong [2 ]
Liao, Feixiong [3 ]
Li, Xinghua [1 ,2 ]
Du, Yuchuan [2 ]
机构：
[1] Urban Mobility Institute, Tongji University, Shanghai,200092, China
[2] Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai,201804, China
[3] Urban Planning and Transportation Group, Eindhoven University of Technology, Netherlands
来源：
Transportation Research Part C: Emerging Technologies | 2022年 / 138卷
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] Online parking assignment in an environment of partially connected vehicles: A multi-agent deep reinforcement learning approach
    Zhang, Xinyuan
    Zhao, Cong
    Liao, Feixiong
    Li, Xinghua
    Du, Yuchuan
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2022, 138
  • [2] Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles
    Kwon, Dohyun
    Kim, Joongheon
    2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,
  • [3] Multi-Agent Deep Reinforcement Learning to Manage Connected Autonomous Vehicles at Tomorrow's Intersections
    Antonio, Guillen-Perez
    Maria-Dolores, Cano
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (07) : 7033 - 7043
  • [4] A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways
    Nakka, Sai Krishna Sumanth
    Chalaki, Behdad
    Malikopoulos, Andreas A.
    2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 3297 - 3302
  • [5] A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles
    Yadav, Pamul
    Mishra, Ashutosh
    Kim, Shiho
    SENSORS, 2023, 23 (10)
  • [6] Cooperative On-Ramp Merging Control of Connected and Automated Vehicles: Distributed Multi-Agent Deep Reinforcement Learning Approach
    Zhou, Shanxing
    Zhuang, Weichao
    Yin, Guodong
    Liu, Haoji
    Qiu, Chunlong
    2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2022, : 402 - 408
  • [7] MULTI-AGENT REINFORCEMENT LEARNING WITH CONTRIBUTIONBASED ASSIGNMENT ONLINE ROUTING IN SDN
    Yue Xiaofeng
    Wu Lijun
    Duan Weiwei
    2022 19TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), 2022,
  • [8] A Multi-Agent Reinforcement Learning Approach for Safe and Efficient Behavior Planning of Connected Autonomous Vehicles
    Han, Songyang
    Zhou, Shanglin
    Wang, Jiangwei
    Pepin, Lynn
    Ding, Caiwen
    Fu, Jie
    Miao, Fei
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 25 (05) : 3654 - 3670
  • [9] Dynamic Controller Assignment in Software Defined Internet of Vehicles Through Multi-Agent Deep Reinforcement Learning
    Yuan, Tingting
    Neto, Wilson da Rocha
    Rothenberg, Christian Esteve
    Obraczka, Katia
    Barakat, Chadi
    Turletti, Thierry
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2021, 18 (01): : 585 - 596
  • [10] Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning
    Palanisamy, Praveen
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
12345