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

Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning