Learning Macro-actions for State-Space Planning

Abstract

Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a solution to learn on-line macro-actions, we propose an algorithm to identify useful macro-actions based on data mining techniques. The integration in the planning search of these learned macro-actions shows significant improvements over four classical planning benchmarks.

Journ{é}es Francophones sur la Planification, la D{é}cision et l'Apprentissage pour la conduite de syst{è}mes (JFPDA 2016) , Jul 2016, Grenoble, France. 2016