  We present the first approach capable of learning domain-independent planning
heuristics entirely from scratch. The heuristics we learn map the hypergraph
representation of the delete-relaxation of the planning problem at hand, to a
cost estimate that approximates that of the least-cost path from the current
state to the goal through the hypergraph. We generalise Graph Networks to
obtain a new framework for learning over hypergraphs, which we specialise to
learn planning heuristics by training over state/value pairs obtained from
optimal cost plans. Our experiments show that the resulting architecture,
STRIPS-HGNs, is capable of learning heuristics that are competitive with
existing delete-relaxation heuristics including LM-cut. We show that the
heuristics we learn are able to generalise across different problems and
domains, including to domains that were not seen during training.
