Coordinated behavior of cooperative agents using deep reinforcement learning
作者: Elhadji Amadou Oury DialloAyumi SugiyamaToshiharu Sugawara
作者单位: 1Department of Computer Science and Communications Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
刊名: Neurocomputing, 2020, Vol.396 , pp.230-240
来源数据库: Elsevier Journal
DOI: 10.1016/j.neucom.2018.08.094
关键词: Deep reinforcement learningMulti-agent systemsCooperationCoordination
原始语种摘要: Abstract(#br)In this work, we focus on an environment where multiple agents with complementary capabilities cooperate to generate non-conflicting joint actions that achieve a specific target. The central problem addressed is how several agents can collectively learn to coordinate their actions such that they complete a given task together without conflicts. However, sequential decision-making under uncertainty is one of the most challenging issues for intelligent cooperative systems. To address this, we propose a multi-agent concurrent framework where agents learn coordinated behaviors in order to divide their areas of responsibility. The proposed framework is an extension of some recent deep reinforcement learning algorithms such as DQN, double DQN, and dueling network architectures....
全文获取路径: Elsevier  (合作)
影响因子:1.634 (2012)

  • learning 学识
  • their 他们的
  • addressed 收信地址
  • proposed 建议的
  • reinforcement 放大
  • according 按照
  • responsibility 责任
  • finally 最后
  • uncertainty 不定
  • cooperative 合啄