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基于强化学习的多任务联盟并行形成策略

蒋建国 苏兆品 齐美彬 张国富

蒋建国, 苏兆品, 齐美彬, 张国富. 基于强化学习的多任务联盟并行形成策略. 自动化学报, 2008, 34(3): 349-352. doi: 10.3724/SP.J.1004.2008.00349
引用本文: 蒋建国, 苏兆品, 齐美彬, 张国富. 基于强化学习的多任务联盟并行形成策略. 自动化学报, 2008, 34(3): 349-352. doi: 10.3724/SP.J.1004.2008.00349
JIANG Jian-Guo, SU Zhao-Pin, QI Mei-Bin, ZHANG Guo-Fu. Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning. ACTA AUTOMATICA SINICA, 2008, 34(3): 349-352. doi: 10.3724/SP.J.1004.2008.00349
Citation: JIANG Jian-Guo, SU Zhao-Pin, QI Mei-Bin, ZHANG Guo-Fu. Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning. ACTA AUTOMATICA SINICA, 2008, 34(3): 349-352. doi: 10.3724/SP.J.1004.2008.00349

基于强化学习的多任务联盟并行形成策略

doi: 10.3724/SP.J.1004.2008.00349
详细信息
    通讯作者:

    苏兆品

Multi-task Coalition Parallel Formation Strategy Based on Reinforcement Learning

More Information
    Corresponding author: SU Zhao-Pin
  • 摘要: Agent coalition is an important manner of agents' coordination and cooperation. Forming a coalition, agents can enhance their ability to solve problems and obtain more utilities. In this paper, a novel multi-task coalition parallel formation strategy is presented, and the conclusion that the process of multi-task coalition formation is a Markov decision process is testified theoretically. Moreover, reinforcement learning is used to solve agents' behavior strategy, and the process of multi-task coalition parallel formation is described. In multi-task oriented domains, the strategy can effectively and parallel form multi-task coalitions.
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出版历程
  • 收稿日期:  2007-05-30
  • 修回日期:  2007-09-21
  • 刊出日期:  2008-03-20

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