GeoDRL: A Self-Learning Framework for Geometry Problem Solving using Reinforcement Learning in Deductive Reasoning
Shuai Peng, Di Fu, Yijun Liang, Liangcai Gao, Zhi Tang
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.
BibTeX
@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://aclanthology.org/2023.findings-acl.850/",
doi = "10.18653/v1/2023.findings-acl.850",
pages = "13468--13480"
}