  Using reinforcement learning for automated theorem proving has recently
received much attention. Current approaches use representations of logical
statements that often rely on the names used in these statements and, as a
result, the models are generally not transferable from one domain to another.
The size of these representations and whether to include the whole theory or
part of it are other important decisions that affect the performance of these
approaches as well as their runtime efficiency. In this paper, we present
NIAGRA; an ensemble Name InvAriant Graph RepresentAtion. NIAGRA addresses this
problem by using 1) improved Graph Neural Networks for learning name-invariant
formula representations that is tailored for their unique characteristics and
2) an efficient ensemble approach for automated theorem proving. Our
experimental evaluation shows state-of-the-art performance on multiple datasets
from different domains with improvements up to 10% compared to the best
learning-based approaches. Furthermore, transfer learning experiments show that
our approach significantly outperforms other learning-based approaches by up to
28%.
