Learning to Reason in Large Theories without Imitation

arXiv: Learning, 2019.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Automated theorem proving in large theories can be learned via reinforcement learning over an indefinitely growing action space. In order to select actions, one performs nearest neighbor lookups in the knowledge base to find premises to be applied. Here we address the exploration for reinforcement learning in this space. Approaches (lik...More

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