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Synthesizing a program that realizes a logical specification is a classicalproblem in computer science. We examine a particular type of program synthesis,where the objective is to synthesize a strategy that reacts to a potentiallyadversarial environment while ensuring that all executions satisfy a LinearTemporal Logic (LTL) specification. Unfortunately, exact methods to solveso-called LTL synthesis via logical inference do not scale. In this work, wecast LTL synthesis as an optimization problem. We employ a neural network tolearn a Q-function that is then used to guide search, and to construct programsthat are subsequently verified for correctness. Our method is unique incombining search with deep learning to realize LTL synthesis. In ourexperiments the learned Q-function provides effective guidance for synthesisproblems with relatively small specifications.

Towards Neural-Guided Program Synthesis for Linear Temporal Logic Specifications