强化学习
计算机科学
自动化
人工智能
装箱问题
人工神经网络
深度学习
虚拟机
可视化
箱子
工业工程
工程类
机械工程
算法
操作系统
作者
Shokhikha Amalana Murdivien,Jumyung Um
出处
期刊:Sensors
[MDPI AG]
日期:2023-08-03
卷期号:23 (15): 6928-6928
摘要
Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement Learning is a potential solution to solve more complex problems by introducing artificial neural networks in Reinforcement Learning. In this paper, a game engine was used for Deep Reinforcement Learning training, which allows visualization of view learning and result processes more intuitively than other tools, as well as a physical engine for a more realistic problem-solving environment. The present research demonstrates that a Deep Reinforcement Learning model can effectively address the real-time sequential 3D bin packing problem by utilizing a game engine to visualize the environment. The results indicate that this approach holds promise for tackling complex logistical challenges in dynamic settings.
科研通智能强力驱动
Strongly Powered by AbleSci AI