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.

通过学习来自 Scratch 的方法，我们提出了第一种能够学习无领域依赖规划启发式的方法。我们通过将规划问题的删除松弛表示的超图映射到通过超图从当前状态到目标的最小成本路径的成本估计来学习启发式。我们将图形网络推广到超图上学习，通过训练从最优成本计划中得到的状态 / 价值对来学习规划启发式。我们的实验表明，生成的 STRIPS-HGN 体系结构能够学习出与 LM-cut 等现有删除松弛启发式竞争的启发式，并且我们学习的启发式能够推广到不同的问题和领域。