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

How can we train neural network (NN) heuristic functions for classicalplanning, using only states as the NN input? Prior work addressed thisquestion by (a) per-instance imitation learning and/or (b) per-domainlearning. The former limits the approach to instances small enough fortraining data generation, the latter to domains where the necessaryknowledge generalizes across instances. Here we explore three methodsfor (a) that make training data generation scalable throughbootstrapping and approximate value iteration. In particular, weintroduce a new bootstrapping variant that estimates search effortinstead of goal distance, which as we show converges to the perfectheuristic under idealized circumstances. We empirically compare thesemethods to (a) and (b), aligning three different NN heuristic functionlearning architectures for cross-comparison in an experiment ofunprecedented breadth in this context. Key lessons are that ourmethods and imitation learning are highly complementary; thatper-instance learning often yields stronger heuristics than per-domainlearning; and the LAMA planner is still dominant but our methodsoutperform it in one benchmark domain.