Learning Domain-Independent Planning Heuristics with Hypergraph Networks
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.
Keywords
Cite
@article{arxiv.1911.13101,
title = {Learning Domain-Independent Planning Heuristics with Hypergraph Networks},
author = {William Shen and Felipe Trevizan and Sylvie Thiébaux},
journal= {arXiv preprint arXiv:1911.13101},
year = {2019}
}