Count-based Novelty Exploration in Classical Planning

TL;DR

This paper introduces a count-based novelty exploration method for classical planning that maintains a constant number of tuples, improving exploration efficiency and solver performance on benchmark problems.

Contribution

It proposes a novel count-based exploration technique with a constant tuple set size and a pruning algorithm, enhancing classical planning search strategies.

Findings

Achieves competitive results on International Planning Competition benchmarks.

Surpasses current state-of-the-art solvers in instance coverage.

Demonstrates effectiveness of the proposed exploration method in classical planning.

Abstract

Count-based exploration methods are widely employed to improve the exploratory behavior of learning agents over sequential decision problems. Meanwhile, Novelty search has achieved success in Classical Planning through recording of the first, but not successive, occurrences of tuples. In order to structure the exploration, however, the number of tuples considered needs to grow exponentially as the search progresses. We propose a new novelty technique, classical count-based novelty, which aims to explore the state space with a constant number of tuples, by leveraging the frequency of each tuple's appearance in a search tree. We then justify the mechanisms through which lower tuple counts lead the search towards novel tuples. We also introduce algorithmic contributions in the form of a trimmed open list that maintains a constant size by pruning nodes with bad novelty values. These techniques are shown to complement existing novelty heuristics when integrated in a classical solver, achieving competitive results in challenging benchmarks from recent International Planning Competitions. Moreover, adapting our solver as the frontend planner in dual configurations that utilize both memory and time thresholds demonstrates a significant increase in instance coverage, surpassing current state-of-the-art solvers.