Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning (Extended Abstract)

Authors

  • Stefan O'Toole The University of Melbourne
  • Miquel Ramirez The University of Melbourne
  • Nir Lipovetzky The University of Melbourne
  • Adrian R. Pearce The University of Melbourne

Keywords:

Machine And Deep Learning In Search, Search In Goal-directed Problem Solving

Abstract

We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems. RSL uses regression to select relevant sets of states at a range of different distances from the goal. RSL then formulates a Supervised Learning problem to obtain the parameters that define the NN heuristic, using the selected states labeled with exact or estimated distances to goal states. Our experimental study shows that RSL outperforms, in terms of coverage, previous classical planning NN heuristics functions while requiring a fraction of the training time.

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Published

2022-07-17