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Enhancing Loop-Invariant Synthesis via Reinforcement Learning

by   Takeshi Tsukada, et al.

Loop-invariant synthesis is the basis of every program verification procedure. Due to its undecidability in general, a tool for invariant synthesis necessarily uses heuristics. Despite the common belief that the design of heuristics is vital for the effective performance of a verifier, little work has been performed toward obtaining the optimal heuristics for each invariant-synthesis tool. Instead, developers have hand-tuned the heuristics of tools. This study demonstrates that we can effectively and automatically learn a good heuristic via reinforcement learning for an invariant synthesizer PCSat. Our experiment shows that PCSat combined with the heuristic learned by reinforcement learning outperforms the state-of-the-art solvers for this task. To the best of our knowledge, this is the first work that investigates learning the heuristics of an invariant synthesis tool.


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