Approximate dynamic programming solutions of multi-agent graphical games using actor-critic network structures

被引:0
|
作者
Abouheaf, Mohammed I. [1 ]
Lewis, Frank L. [1 ]
机构
[1] University of Texas, Arlington Research Institute, University of Texas at Arlington, 7300 Jack Newell Blvd. S., Ft. Worth, TX 76118, United States
关键词
Engineering Village;
D O I
2013 International Joint Conference on Neural Networks, IJCNN 2013
中图分类号
学科分类号
摘要
Approximate dynamic programming - Dual heuristic programming - Hamilton Jacobi Bellman equation - Heuristic dynamic programming - Neighborhood information - Neural network structures - Optimal control theory - Reinforcement learning techniques
引用
收藏
相关论文
共 50 条
  • [1] Approximate Dynamic Programming Solutions of Multi-Agent Graphical Games Using Actor-Critic Network Structures
    Abouheaf, Mohammed I.
    Lewis, Frank L.
    2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2013,
  • [2] Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games
    Malloy, Tyler
    Sims, Chris R.
    Klinger, Tim
    Liu, Miao
    Riemer, Matthew
    Tesauro, Gerald
    2021 IEEE CONFERENCE ON GAMES (COG), 2021, : 332 - 339
  • [3] Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
    Ryu, Heechang
    Shin, Hayong
    Park, Jinkyoo
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 7236 - 7243
  • [4] A multi-agent reinforcement learning using Actor-Critic methods
    Li, Chun-Gui
    Wang, Meng
    Yuan, Qing-Neng
    PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 878 - 882
  • [5] B -Level Actor-Critic for Multi-Agent Coordination
    Zhang, Haifeng
    Chen, Weizhe
    Huang, Zeren
    Li, Minne
    Yang, Yaodong
    Zhang, Weinan
    Wang, Jun
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 7325 - 7332
  • [6] Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
    Xiao, Yuchen
    Tan, Weihao
    Amato, Christopher
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
  • [7] Divergence-Regularized Multi-Agent Actor-Critic
    Su, Kefan
    Lu, Zongqing
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 2022,
  • [8] An Object Oriented Approach to Fuzzy Actor-Critic Learning for Multi-Agent Differential Games
    Schwartz, Howard
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 183 - 190
  • [9] An actor-critic algorithm for multi-agent learning in queue-based stochastic games
    Sundar, D. Krishna
    Ravikumar, K.
    NEUROCOMPUTING, 2014, 127 : 258 - 265
  • [10] Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning
    Diddigi, Raghuram Bharadwaj
    Reddy, D. Sai Koti
    Prabuchandran, K. J.
    Bhatnagar, Shalabh
    AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, 2019, : 1931 - 1933