Research Progress of Multi-Agent Deep Reinforcement Learning

被引：0
|
作者：
Ding, Shi-Feiu [1 ,2 ]
Du, Weiu [1 ]
Zhang, Jianu [1 ,2 ]
Guo, Li-Liu [1 ,2 ]
Ding, Ding [3 ]
机构：
[1] School of Computer Science and Technology, China University of Mining and Technology, Jiangsu, Xuzhou,221116, China
[2] Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Jiangsu, Xuzhou,221116, China
[3] College of Intelligence and Computing, Tianjin University, Tianjin,300350, China
来源：
Jisuanji Xuebao/Chinese Journal of Computers | 2024年 / 47卷 / 07期
关键词：
D O I：
10.11897/SP.J.1016.2024.01547
中图分类号：
学科分类号：
摘要：
118
引用
下载
收藏
页码：1547 / 1567
相关论文
共 50 条
  • [1] 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
  • [2] 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
  • [3] Multi-agent deep reinforcement learning: a survey
    Sven Gronauer
    Klaus Diepold
    Artificial Intelligence Review, 2022, 55 : 895 - 943
  • [4] 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
  • [5] Multi-agent deep reinforcement learning: a survey
    Gronauer, Sven
    Diepold, Klaus
    ARTIFICIAL INTELLIGENCE REVIEW, 2022, 55 (02) : 895 - 943
  • [6] 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
  • [7] 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
  • [8] MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
    Malysheva, Aleksandra
    Kudenko, Daniel
    Shpilman, Aleksei
    2019 XVI INTERNATIONAL SYMPOSIUM PROBLEMS OF REDUNDANCY IN INFORMATION AND CONTROL SYSTEMS (REDUNDANCY), 2019, : 171 - 176
  • [9] A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning
    Yi Liu
    Xiang Wu
    Yuming Bo
    Jiacun Wang
    Lifeng Ma
    IEEE/CAA Journal of Automatica Sinica, 2024, 11 (11) : 2346 - 2348
  • [10] A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning
    Liu, Yi
    Wu, Xiang
    Bo, Yuming
    Wang, Jiacun
    Ma, Lifeng
    IEEE/CAA Journal of Automatica Sinica, 2024, 11 (11) : 2346 - 2348
12345