Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods
Patrick Ferber, Florian Geißer, Felipe Trevizan, Malte Helmert and Joerg Hoffmann
Abstract: How can we train neural network (NN) heuristic functions for classical
planning, using only states as the NN input? Prior work addressed this
question by (a) per-instance imitation learning and/or (b) per-domain
learning. The former limits the approach to instances small enough for
training data generation, the latter to domains where the necessary
knowledge generalizes across instances. Here we explore three methods
for (a) that make training data generation scalable through
bootstrapping and approximate value iteration. In particular, we
introduce a new bootstrapping variant that estimates search effort
instead of goal distance, which as we show converges to the perfect
heuristic under idealized circumstances. We empirically compare these
methods to (a) and (b), aligning three different NN heuristic function
learning architectures for cross-comparison in an experiment of
unprecedented breadth in this context. Key lessons are that our
methods and imitation learning are highly complementary; that
per-instance learning often yields stronger heuristics than per-domain
learning; and the LAMA planner is still dominant but our methods
outperform it in one benchmark domain.
*This password protected talk video will only be available after it was presented at the conference.
