Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods

Authors

  • Patrick Ferber University of Basel, Basel, Switzerland Saarland University, Saarland Informatics Campus, Saarbrücken, Germany
  • Florian Geißer The Australian National University, Canberra, Australia
  • Felipe Trevizan The Australian National University, Canberra, Australia
  • Malte Helmert University of Basel, Basel, Switzerland
  • Jörg Hoffmann Saarland University, Saarland Informatics Campus, Saarbrücken, Germany German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany

Keywords:

Classical Planning, Heuristic Search, Learning Heuristic Functions

Abstract

How can we train neural network (NN) heuristic functions for classical planning, using only states as the NN input? Prior work addressed this question by (a) per-instance imitation learning and/or (b) per-domain learning. The former limits the approach to instances small enough for training data generation, the latter to domains where the necessary knowledge generalizes across instances. Here we explore three methods for (a) that make training data generation scalable through bootstrapping and approximate value iteration. In particular, we introduce a new bootstrapping variant that estimates search effort instead of goal distance, which as we show converges to the perfect heuristic under idealized circumstances. We empirically compare these methods to (a) and (b), aligning three different NN heuristic function learning architectures for cross-comparison in an experiment of unprecedented breadth in this context. Key lessons are that our methods and imitation learning are highly complementary; that per-instance learning often yields stronger heuristics than per-domain learning; and the LAMA planner is still dominant but our methods outperform it in one benchmark domain.

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Published

2022-06-13

How to Cite

Ferber, P., Geißer, F., Trevizan, F., Helmert, M., & Hoffmann, J. (2022). Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 583-587. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/19845