An Approach to the Piano Mover's Problem Using Hierarchic Reinforcement Learning(Distributed Cooperation and Agents)

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概要

• 論文の詳細を見る

• We attempt to achieve corporative behavior of autonomous decentralized agents constructed via Q-Learning, which is a type of reinforcement learning. As such, in the present paper, we examine the piano mover's problem including a find-path problem. We propose a multiagent architecture that has an external agent and internal agents. Internal agents are homogenous and can communicate with each other. The movement of the external agent depends on the composition of the actions of the internal agents. By learning how to move through the internal agents, avoidance of obstacles by the object is expected. We simulate the proposed method in a two-dimensional continuous world. Results obtained in the present investigation reveal the effectiveness of the proposed method.

• 社団法人電子情報通信学会の論文
• 2004-08-01

著者

• Yoshida Tomohiro

  Exploratory Research Ii Drug Discovery Laboratories Yoshitomi Pharmaceutical Industries Ltd.

• Yokoi Hiroshi

  Faculty Of Complex Systems Engineering Hokkaido University

• Kakazu Y

  Faculty Of Complex Systems Engineering Hokkaido University

• Kakazu Yukinori

  Faculty Of Engineering Hokkaido University

• ISHIWAKA Yuko

  Hakodate Institute of National College of Technology

• YOSHIDA Tomohiro

  Hakodate Institute of National College of Technology

• Yoshida T

  Hakodate Institute Of National College Of Technology

• KAKAZU Yukinori

  Faculty of Complex Systems Engineering, Hokkaido University

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