  We propose and evaluate a system which learns a neuralnetwork heuristic
function for forward search-based, satisficing classical planning. Our system
learns distance-to-goal estimators from scratch, given a single PDDL training
instance. Training data is generated by backward regression search or by
backward search from given or guessed goal states. In domains such as the
24-puzzle where all instances share the same search space, such heuristics can
also be reused across all instances in the domain. We show that this relatively
simple system can perform surprisingly well, sometimes competitive with
well-known domain-independent heuristics.
