Improving exploration in policy gradient search: Application to symbolic optimization
This work addresses exploration issues in symbolic optimization for researchers and practitioners, representing an incremental improvement over existing reinforcement learning approaches.
The paper tackled the problem of limited exploration in neural network-based symbolic optimization by introducing two exploration methods, which improved performance, increased sample efficiency, and lowered solution complexity for symbolic regression tasks.
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols. In contrast to traditional evolutionary approaches, using a neural network at the core of the search allows learning higher-level symbolic patterns, providing an informed direction to guide the search. When no labeled data is available, such networks can still be trained using reinforcement learning. However, we demonstrate that this approach can suffer from an early commitment phenomenon and from initialization bias, both of which limit exploration. We present two exploration methods to tackle these issues, building upon ideas of entropy regularization and distribution initialization. We show that these techniques can improve the performance, increase sample efficiency, and lower the complexity of solutions for the task of symbolic regression.