强化学习
计算机科学
自动引导车
人工智能
产品(数学)
机器学习
数学
几何学
作者
Mohamed Rhazzaf,Tawfik Masrour
出处
期刊:Advances in intelligent systems and computing
日期:2020-09-02
卷期号:: 227-237
被引量:2
标识
DOI:10.1007/978-3-030-51186-9_16
摘要
Automated guided vehicles system (AGVS) is a new logistics problem area, and the most demanded in terms of performance in view of the exponential growth of product traffic in the world. The present paper aims to to propose a new approach, to deal with AGVS problems, based on deep reinforcement learning algorithms, as an alternative to classic methods. Indeed, the classical approaches are based on a pre-established policy of vehicle movement rules, while our method deduces the movement rules based on trial/error reinforcement learning approach, and effectively gives good results in relatively small moving areas and a limited number of agents.
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