Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections.

Additionally,wepropose theGraphCroc, anewGAE thatsupports flexible encoder architectures tailored forvarious downstream tasksand ensures robust structural reconstruction, through a mirrored encoding-decoding process.

Wephrase the challenge ofsolving SMT formulas asatree search problemwhere ateach step atransformation is applied to the input formula until the formula is solved.

Moreover, weassumethatthedimensionsd and k are fixedthroughoutthispaper.

Van Seijen et al. [2017] propose to split a state into different sub-states, each with a sub-reward obtained bytraining ageneral valuefunction, andlearnmultiple valuefunctions withsub-rewards. The architecture is rather limited due to requiring prior knowledge of how to split into sub-states.

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