@inproceedings{peng-etal-2023-geodrl,
    title = "{G}eo{DRL}: A Self-Learning Framework for Geometry Problem Solving using Reinforcement Learning in Deductive Reasoning",
    author = "Peng, Shuai  and
      Fu, Di  and
      Liang, Yijun  and
      Gao, Liangcai  and
      Tang, Zhi",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.findings-acl.850/",
    doi = "10.18653/v1/2023.findings-acl.850",
    pages = "13468--13480",
    abstract = "Ensuring both interpretability and correctness is a great challenge in automated geometry problem solving (GPS), and the scarcity of labeled data hinders learning mathematical reasoning from samples. Therefore, we present GeoDRL, a self-learning geometry problem solving framework that integrates logic graph deduction and Deep Reinforcement Learning (DRL) to optimize geometry reasoning as a Markov Decision Process. GeoDRL employs a Graph Neural Network on a Geometry Logic Graph, updating the problem state using a symbolic system. Incorporating DRL into deductive reasoning enables GeoDRL to achieve unsupervised self-learning while maintaining correctness. GeoDRL, through unsupervised learning, exhibits enhanced accuracy in the Geometry3K dataset, improving by 11.1{\%} over previous SOTA methods, and simultaneously boosts efficiency and interpretability."
}