Learning Search-Space Specific Heuristics Using Neural Network

Anonymous

Keywords: heuristic search, neural network

TL;DR: proposal and evaluation of a method for generating training data for a neural network based search heuristic

Abstract: We propose and evaluate a system which learns a neural-network 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.