Optimal routing control of a construction machine by deep reinforcement learning

Zeyuan Sun, Masayuki Nakatani, Yutaka Uchimura

研究成果: Conference contribution

抜粋

Deep reinforcement learning algorithms are rapidly growing, and expected to be applied to many industrial fields. In this paper, we proposed a method that combines a deep Q-network with batch normalization to generate an optimal route for a grading machine. The goal is to achieve autonomous operation of the grading machine. For the learning platform, a grading simulator was developed to emulate the grading work. The proposed method was evaluated with the grading simulator, and showed better route searching performance results than the conventional method.

元の言語English
ホスト出版物のタイトルProceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018
出版者Institute of Electrical and Electronics Engineers Inc.
ページ187-192
ページ数6
ISBN(電子版)9781538619469
DOI
出版物ステータスPublished - 2018 6 1
イベント15th IEEE International Workshop on Advanced Motion Control, AMC 2018 - Tokyo, Japan
継続期間: 2018 3 92018 3 11

Other

Other15th IEEE International Workshop on Advanced Motion Control, AMC 2018
Japan
Tokyo
期間18/3/918/3/11

    フィンガープリント

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Mechanical Engineering
  • Control and Optimization

これを引用

Sun, Z., Nakatani, M., & Uchimura, Y. (2018). Optimal routing control of a construction machine by deep reinforcement learning. : Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018 (pp. 187-192). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AMC.2019.8371085