Optimal Routing Control of a Construction Machine by Deep Reinforcement Learning

被引：0

作者：

Sun, Zeyuan ^{[1
]}

Nakatani, Masayuki ^{[1
]}

Uchimura, Yutaka ^{[1
]}

机构：

[1] Shibaura Inst Technol, Koto Ku, 3-7-5 Toyosu, Tokyo, Japan

来源：

2018 IEEE 15TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC) | 2018年

基金：

日本科学技术振兴机构;

关键词：

deep reinforcement learning; grading machine; artificial intelligence; batch normalization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：187 / 192

页数：6

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