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Many machine learning strategies designed to automate mathematical tasksleverage neural networks to search large combinatorial spaces of mathematicalsymbols. In contrast to traditional evolutionary approaches, using a neuralnetwork at the core of the search allows learning higher-level symbolicpatterns, providing an informed direction to guide the search. When no labeleddata is available, such networks can still be trained using reinforcementlearning. However, we demonstrate that this approach can suffer from an earlycommitment phenomenon and from initialization bias, both of which limitexploration. 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 sampleefficiency, and lower the complexity of solutions for the task of symbolicregression.

Improving exploration in policy gradient search: Application to symbolic optimization