BoxStacker:在物流系统的虚拟环境中的3D垃圾箱包装问题深度强化学习
Shokhikha Amalana Murdivien1, Jumyung Um1
1Department of Industrial and Management System Engineering, Kyung Hee University, 1732 Deogyeong-daero, Yongin-si 17104, Republic of Korea.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
制造系统可以使用深度强化学习来实现弹性,自我组织的操作. 游戏引擎的方法有效地解决了实时的3D垃圾包装,优化了动态供应链中的物流.
科学领域:
- 机器人和自动化机器人与自动化
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 制造系统需要增强性和自我组织,以管理供应链中断.
- 由于缺乏人类专家,物流中的自动化增加需要先进的解决方案.
- 深度强化学习 (DRL) 通过整合人工神经网络,为复杂的问题解决提供了强大的方法.
研究的目的:
- 研究深度强化学习在实时顺序3D垃圾包装中的应用.
- 利用游戏引擎进行直观可视化和对物流中的DRL模型进行现实的训练.
- 证明DRL在应对动态物流挑战中的有效性.
主要方法:
- 使用了与物理引擎集成的游戏引擎,以实现现实的模拟环境.
- 训练了一个深度强化学习模型来解决连续的3D垃圾包装问题.
- 在游戏引擎中使用可视化工具,以直观地观察学习和结果.
主要成果:
- 深度强化学习模型成功地解决了实时顺序的3D垃圾包装问题.
- 游戏引擎为DRL培训和验证提供了直观和现实的环境.
- 拟议的方法在动态的物流环境中表现出有效性.
结论:
- 深度增强学习,结合游戏引擎可视化,是复杂的,实时后勤问题的有希望的解决方案.
- 这种方法提高了制造系统的适应性和自我组织性.
- 该方法在动态环境中优化自动化物流方面具有重大潜力.
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