交互式符号回归与共同设计机制通过离线强化学习
Yuan Tian1,2, Wenqi Zhou1,3, Michele Viscione3
1Institute for Building Materials, ETH Zürich, Zürich, Switzerland.
Nature communications
|April 25, 2025
概括
符号回归 (SR) 现在可以通过使用符号Q网络的专家知识来增强. 这种交互式框架改善了方程发现,在现实世界的应用中表现优于标准模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 科学发现 科学发现 发现
背景情况:
- 符号回归 (SR) 旨在从数据中找到数学表达式.
- 现有的SR方法在很大的搜索空间和集成领域知识方面扎.
- 在SR中的变压器模型往往缺乏专家交互能力.
研究的目的:
- 引入象征性Q网络,这是大规模SR的交互性框架.
- 利用强化学习和专家共同设计来改善方程发现.
- 克服现有的基于变压器的SR方法的局限性.
主要方法:
- 开发了一个新的交互式SR框架Symbolic Q-network.
- 在没有基于变压器的解码器的情况下利用强化学习.
- 实施了共同设计机制,以实现无专家在循环中的集成.
主要成果:
- 象征性Q网络的表现与在基准上预先训练的模型相美.
- 交互式共同设计显著提高了对现实世界问题的性能.
- 与标准自回归模型相比,实现了优越的性能增长.
结论:
- 象征性Q网络为SR提供了一种有效的交互式方法.
- 通过共同设计整合领域专家知识对于SR成功至关重要.
- 这个框架推动了自动化科学发现领域的发展.
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