  Despite the recent successes of deep neural networks in various fields such
as image and speech recognition, natural language processing, and reinforcement
learning, we still face big challenges in bringing the power of numeric
optimization to symbolic reasoning. Researchers have proposed different avenues
such as neural machine translation for proof synthesis, vectorization of
symbols and expressions for representing symbolic patterns, and coupling of
neural back-ends for dimensionality reduction with symbolic front-ends for
decision making. However, these initial explorations are still only point
solutions, and bear other shortcomings such as lack of correctness guarantees.
In this paper, we present our approach of casting symbolic reasoning as games,
and directly harnessing the power of deep reinforcement learning in the style
of Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT)
problem as showcase, we demonstrate the feasibility of our method, and the
advantages of modularity, efficiency, and correctness guarantees.
