混合残留多专家强化学习用于高密度停车场的空间调度
IEEE transactions on cybernetics
|October 23, 2023
概括
我们开发了一种混合残留多专家强化学习 (HIRE RL) 方法,以解决工业元宇宙中复杂的高密度停车安排问题,显著提高效率并减少车辆机动.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 城市规划 城市规划
背景情况:
- 工业正在采用元宇宙来提高生产力,特别是在复杂的调度中.
- 高密度停车 (HDP) 是解决城市停车挑战的解决方案,利用基于堆的系统和机器人.
- 现有的HDP调度算法通常是启发式的,缺乏效率和有效的信息利用.
研究的目的:
- 提出一种新的混合残留多专家强化学习 (HIRE RL) 方法,以实现高效的HDP批次空间调度.
- 在复杂的工业元宇宙应用中解决当前启发式调度方法的局限性.
- 在高密度停车系统中提高车辆机动效率和稳定性.
主要方法:
- 开发了一个HIRE RL框架,其中启发式方法作为专家.
- 利用RL训练的神经网络,根据停车场状态选择专家策略.
- 整合了一个带有剩余输出通道的层次网络,以克服启发式专家限制.
主要成果:
- 在减少车辆机动方面,HIRE RL算法超过了先进的启发式方法和端到端的RL.
- 对停车场大小的变化和车辆退出时间估计的准确性表现出良好的稳定性.
- HIRE RL为复杂的HDP批量空间调度问题提供了高效的解决方案.
结论:
- 拟议的HIRE RL方法对于工业元宇宙应用来说是有效和实用的.
- 这项工作代表了强化学习在工业元宇宙中的实际应用的重要一步.
- HIRE RL提高了高密度停车场管理系统的效率和稳定性.
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