Learning Domain-Independent Planning Heuristics with Hypergraph Networks

William Shen, Felipe Trevizan, Sylvie Thiébaux

PosterID: 40
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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, STRIPSHGNs, is capable of learning heuristics that are competitive with existing delete-relaxation heuristics including LM-cut. We show that heuristics we learn are able to generalise across different problems and domains, including to domains that were not seen during training.

Session Aus3+Aus5: Probabilistic Planning & Learning
Canb 10/28/2020, 11:00 – 12:15
10/29/2020, 20:00 – 21:15
Paris 10/28/2020, 01:00 – 02:15
10/29/2020, 10:00 – 11:15
NYC 10/27/2020, 20:00 – 21:15
10/29/2020, 05:00 – 06:15
LA 10/27/2020, 17:00 – 18:15
10/29/2020, 02:00 – 03:15