Curiosity-driven RL for symbolic equation solving
This work addresses symbolic reasoning tasks for AI systems, but it appears incremental as it builds on prior methods for linear equations.
The paper tackled the problem of using reinforcement learning for symbolic mathematics, showing that model-free PPO with curiosity-based exploration and graph-based actions can solve nonlinear equations involving radicals, exponentials, and trig functions.
We explore if RL can be useful for symbolic mathematics. Previous work showed contrastive learning can solve linear equations in one variable. We show model-free PPO \cite{schulman2017proximal} augmented with curiosity-based exploration and graph-based actions can solve nonlinear equations such as those involving radicals, exponentials, and trig functions. Our work suggests curiosity-based exploration may be useful for general symbolic reasoning tasks.