汎化能力を有する強化学習による最適経路問題の解法

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2019/12/01

タイトル(英語): Solution of an Optimal Routing Problem by Reinforcement Learning with Generalization Ability

著者名: 飯間　等（京都工芸繊維大学），大西　鴻哉（京都工芸繊維大学）

著者名(英語): Hitoshi Iima (Kyoto Institute of Technology), Hiroya Oonishi (Kyoto Institute of Technology)

キーワード: 強化学習，機械学習，汎化能力，最適経路問題　　reinforcement learning，machine learning，generalization ability，optimal routing problem

要約(英語): In applying reinforcement learning to a different environment, relearning is generally required. The relearning, however, is time-consuming, and therefore a method without the relearning should be developed. This paper proposes a reinforcement learning method with generalization ability for solving an optimal routing problem with a given set of multiple goal positions. The proposed method can rapidly find the optimal route for any set of the multiple goal positions once a reinforcement learning agent learns. In the proposed method, a graph search algorithm determines the visiting order of the goal positions, and an ordinary learning algorithm such as Q-learning determines each route between goal positions. The performance of the proposed method is evaluated through numerical experiments.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.139 No.12 （2019） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1494-1500 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/ieejeiss/139/12/139_1494/_article/-char/ja/