  We present RL-VAE, a graph-to-graph variational autoencoder that uses
reinforcement learning to decode molecular graphs from latent embeddings.
Methods have been described previously for graph-to-graph autoencoding, but
these approaches require sophisticated decoders that increase the complexity of
training and evaluation (such as requiring parallel encoders and decoders or
non-trivial graph matching). Here, we repurpose a simple graph generator to
enable efficient decoding and generation of molecular graphs.
