深層強化学習による施工機械の経路最適化

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2019/04/01

タイトル(英語): Route Optimization of Construction Machine by Deep Reinforcement Learning

著者名: 田邉　峻也（芝浦工業大学），孫　澤源（芝浦工業大学），中谷　優之（芝浦工業大学），内村　裕（芝浦工業大学）

著者名(英語): Shunya Tanabe (Shibaura Institute of Technology), Zeyuan Sun (Shibaura Institute of Technology), Masayuki Nakatani (Shibaura Institute of Technology), Yutaka Uchimura (Shibaura Institute of Technology)

キーワード: 深層強化学習，人工知能，機械学習，自律制御，整地機械　　deep reinforcement learning，artificial intelligence，machine learning，autonomous control，leveling machine

要約(英語): After it was reported that an AI player scored higher in Atari2600 games than skilled human players by using deep reinforcement learning techniques, many researchers were inspired to apply deep reinforcement leaning in various fields. This paper focuses on the autonomous ground leveling work by a bulldozer, which is expected to optimize the action of the bulldozer. In a previous work, we implemented a deep Q learning method by giving the images as the input data for the network. However, when learning the image using the convolution layer as the input using deep reinforcement learning, it requires a large computational cost for the learning process. If the size of the neural network is shrunken by contriving the data to be supplied to the input, the learning time (duration) will be reduced. This paper describes the comparison results for different orders of input data. the transition of the learning sequence is also evaluated.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.139 No.4 （2019） 特集：平成30年産業応用部門大会

本誌掲載ページ: 401-408 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/139/4/139_401/_article/-char/ja/