Task-Oriented Reinforcement Learning for Continuing Task in Dynamic Environment

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  • Kamal Md.Abdus Samad
    Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University : Graduate Student
  • 村田 純一
    九州大学大学院システム情報科学研究院電気電子システム工学部門
  • 平澤 宏太郎
    早稲田大学大学院情報生産システム研究科

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This paper presents task-oriented reinforcement learning, a modified approach of reinforcement-learning to simplify continuing dynamic problems in a more realistic and humanlike way of thinking from the viewpoint of the tasks. In this learning method an agent takes as input the `state of task' instead of 'state of environment' and chooses appropriate action to achieve the goal of the corresponding task. The proposed system learns from the viewpoint of tasks that enables the system to find and follow a precise policy in a continuing-dynamic environment and offers simple implementation for a multiple agents system.

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