On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)

被引：0
|
作者：
Mondal, Washim Uddin [1 ]
Agarwal, Mridul [2 ]
Aggarwal, Vaneet [3 ]
Ukkusuri, Satish V. [4 ]
机构：
[1] Lyles School of Civil Engineering, School of Industrial Engineering, Purdue University, West Lafayette,IN,47907, United States
[2] School of Electrical and Computer Engineering, Purdue University, West Lafayette,IN,47907, United States
[3] School of Industrial Engineering, School of Electrical and Computer Engineering, Purdue University, West Lafayette,IN,47907, United States
[4] Lyles School of Civil Engineering, Purdue University, West Lafayette,IN,47907, United States
来源：
Journal of Machine Learning Research | 2022年 / 23卷
关键词：
Compilation and indexing terms; Copyright 2024 Elsevier Inc;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Fertilizers - Multi agent systems - Software agents - Reinforcement learning
引用
收藏
相关论文
共 16 条
  • [1] marl-jax: Multi-agent Reinforcement Leaning Framework
    Mehta, Kinal
    Mahajan, Anuj
    Kumar, Pawan
    arXiv, 2023,
  • [2] Eavesdropping Game Based on Multi-Agent Deep Reinforcement Learning
    Guo, Delin
    Tang, Lan
    Yang, Lvxi
    Liang, Ying-Chang
    IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, 2022, 2022-July
  • [3] Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization
    Mendo, Adriano
    Outes-Carnero, Jose
    Ng-Molina, Yak
    Ramiro-Moreno, Juan
    IAENG International Journal of Computer Science, 2023, 50 (03)
  • [4] Optimized distributed formation control using identifier–critic–actor reinforcement learning for a class of stochastic nonlinear multi-agent systems
    Shandong University of Aeronautics, Binzhou, Shandong
    256600, China
    不详
    116024, China
    ISA Trans, 1600, (1-10):
  • [5] Secure Underwater Distributed Antenna Systems: A Multi-Agent Reinforcement Learning Approach
    Wang, Chaofeng
    Bi, Zhicheng
    Wan, Yaping
    IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2023, 10 (07) : 1622 - 1624
  • [6] A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning
    Cui, Kai
    Tahir, Anam
    Ekinci, Gizem
    Elshamanhory, Ahmed
    Eich, Yannick
    Li, Mengguang
    Koeppl, Heinz
    arXiv, 2022,
  • [7] TMAPath: A Knowledge Inference Method Based on Transfer Learning and Multi-agent Deep Reinforcement Learning
    He, Dairao
    Hu, Jingfeng
    Journal of Network Intelligence, 2022, 7 (03): : 574 - 591
  • [8] Distributed adaptive consensus tracking control for heterogeneous nonlinear multi-agent systems
    He, Lei
    Dong, Wenhan
    ISA Transactions, 2022, 130 : 177 - 183
  • [9] Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-Agent Systems with Nonidentical Dimensions
    Qin, Bo
    Fan, Yongqing
    Gao, Yang
    Tehnicki Vjesnik, 2022, 29 (05): : 1537 - 1544
  • [10] Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem
    Shu, Xiulan
    Lin, Anping
    Wen, Xupeng
    Sensors, 24 (20):
12