在强化学习中探索层次世界模型的局限性
Robin Schiewer1, Anand Subramoney2, Laurenz Wiskott3
1Department of Computer Science, Institute for Neural Computation, Ruhr-University Bochum, Bochum, 44787, Germany. robin.schiewer@ini.rub.de.
Scientific reports
|November 5, 2024
概括
本研究引入了一种新的基于层次模型的强化学习 (HMBRL) 框架,采用静态时间抽象. 虽然它使多层次决策成为可能,但对抽象模型利用的挑战被确定为未来的研究.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 基于模型的层次增强学习 (HMBRL) 结合了基于模型和层次的方法,以提高样本效率和抽象.
- 当前的HMBRL框架面临的复杂性阻碍了一般原则的提取和适应.
- 在理解和应用各种用例方面存在挑战,阻碍了HMBRL的进展.
研究的目的:
- 介绍和评估一个新的HMBRL框架.
- 探索静态和环境不可知的时间抽象,用于并发培训.
- 在层次结构中解决抽象模型利用的挑战.
主要方法:
- 构建了具有不同时间抽象水平的等级世界模型.
- 训练了一堆特工,从顶部向下传达目标.
- 专注于静态的,环境不可知的时间抽象,用于低维的抽象行为.
- 评估了框架能够促进跨抽象层次的决策的能力.
主要成果:
- 拟议的HMBRL方法促进了跨两个抽象级别的决策.
- 静态时间抽象允许模型和代理人的同时训练.
- 该框架在最后一集的回报中没有超过传统方法.
- 世界模型堆的抽象层面上的模型利用成为一个关键的挑战.
结论:
- 新的HMBRL框架表明了多层次决策的潜力.
- 静态时间抽象在同时培训和行动空间维度方面提供了优势.
- 需要进行进一步的研究,以应对用于性能提升的抽象模型利用方面的挑战.
- 这项工作有助于完善HMBRL方法和了解其局限性.
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