  We develop Stratified Shortest Solution Imitation Learning (3SIL) to learn
equational theorem proving in a deep reinforcement learning (RL) setting. The
self-trained models achieve state-of-the-art performance in proving problems
generated by one of the top open conjectures in quasigroup theory, the Abelian
Inner Mapping (AIM) Conjecture. To develop the methods, we first use two
simpler arithmetic rewriting tasks that share tree-structured proof states and
sparse rewards with the AIM problems. On these tasks, 3SIL is shown to
significantly outperform several established RL and imitation learning methods.
The final system is then evaluated in a standalone and cooperative mode on the
AIM problems. The standalone 3SIL-trained system proves in 60 seconds more
theorems (70.2%) than the complex, hand-engineered Waldmeister system (65.5%).
In the cooperative mode, the final system is combined with the Prover9 system,
proving in 2 seconds what standalone Prover9 proves in 60 seconds.
