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. | |
| 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 |
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