Both discriminator D ϕ and reward R ψ networks take a molecular graph (represented as atom matrices and bond tensors) as input and returns a scalar ∈ (-∞, ∞) for D and ∈ [0, 1] for R. We use the same architecture (based on Relational-GCN by Schlichtkrull et al. ) for both, but keep their parameters separate .