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
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
  • [21] Multi-Agent Deep Reinforcement Learning for content caching within the Internet of Vehicles
    Knari, Anas
    Derfouf, Mostapha
    Koulali, Mohammed-Amine
    Khoumsi, Ahmed
    AD HOC NETWORKS, 2024, 152
  • [22] Multi-Agent Deep Reinforcement Learning Based Scheduling Approach for Mobile Charging in Internet of Electric Vehicles
    Liu, Linfeng
    Huang, Zhuo
    Xu, Jia
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2024, 23 (10) : 10130 - 10145
  • [23] HALFTONING WITH MULTI-AGENT DEEP REINFORCEMENT LEARNING
    Jiang, Haitian
    Xiong, Dongliang
    Jiang, Xiaowen
    Yin, Aiguo
    Ding, Li
    Huang, Kai
    2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2022, : 641 - 645
  • [24] Deep reinforcement learning for multi-agent interaction
    Ahmed, Ibrahim H.
    Brewitt, Cillian
    Carlucho, Ignacio
    Christianos, Filippos
    Dunion, Mhairi
    Fosong, Elliot
    Garcin, Samuel
    Guo, Shangmin
    Gyevnar, Balint
    McInroe, Trevor
    Papoudakis, Georgios
    Rahman, Arrasy
    Schafer, Lukas
    Tamborski, Massimiliano
    Vecchio, Giuseppe
    Wang, Cheng
    Albrecht, Stefano, V
    AI COMMUNICATIONS, 2022, 35 (04) : 357 - 368
  • [25] Multi-agent deep reinforcement learning: a survey
    Sven Gronauer
    Klaus Diepold
    Artificial Intelligence Review, 2022, 55 : 895 - 943
  • [26] Deep Multi-Agent Reinforcement Learning: A Survey
    Liang X.-X.
    Feng Y.-H.
    Ma Y.
    Cheng G.-Q.
    Huang J.-C.
    Wang Q.
    Zhou Y.-Z.
    Liu Z.
    Zidonghua Xuebao/Acta Automatica Sinica, 2020, 46 (12): : 2537 - 2557
  • [27] Lenient Multi-Agent Deep Reinforcement Learning
    Palmer, Gregory
    Tuyls, Karl
    Bloembergen, Daan
    Savani, Rahul
    PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18), 2018, : 443 - 451
  • [28] Multi-agent deep reinforcement learning: a survey
    Gronauer, Sven
    Diepold, Klaus
    ARTIFICIAL INTELLIGENCE REVIEW, 2022, 55 (02) : 895 - 943
  • [29] Learning to Communicate with Deep Multi-Agent Reinforcement Learning
    Foerster, Jakob N.
    Assael, Yannis M.
    de Freitas, Nando
    Whiteson, Shimon
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), 2016, 29
  • [30] Multi-Agent Reinforcement Learning for Autonomous On Demand Vehicles
    Boyali, Ali
    Hashimoto, Naohisa
    John, Vijay
    Acarman, Tankut
    2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19), 2019, : 1461 - 1468