Learning Domain-Independent Planning Heuristics with Hypergraph Networks

Authors

  • William Shen The Australian National University
  • Felipe Trevizan The Australian National University
  • Sylvie Thiébaux The Australian National University

DOI:

https://doi.org/10.1609/icaps.v30i1.6754

Abstract

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.

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Published

2020-06-01

How to Cite

Shen, W., Trevizan, F., & Thiébaux, S. (2020). Learning Domain-Independent Planning Heuristics with Hypergraph Networks. Proceedings of the International Conference on Automated Planning and Scheduling, 30(1), 574-584. https://doi.org/10.1609/icaps.v30i1.6754