Efficient Policy Construction for MDPs Represented in Probabilistic PDDL

Boris Lesner (1) , Bruno Zanuttini (2)

1 GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
2 Equipe MAD - Laboratoire GREYC - UMR6072

We present a novel dynamic programming approach to computing optimal policies for Markov Decision Processes compactly represented in grounded Probabilistic PDDL. Unlike other approaches, which use an intermediate representation as Dynamic Bayesian Networks, we directly exploit the PPDDL description by introducing dedicated backup rules. This provides an alternative approach to DBNs, especially when actions have highly correlated effects on variables. Indeed, we show significant improvements on several planning domains from the International Planning Competition. Finally, we exploit the incremental flavor of our backup rules for designing promising approaches to policy revision.

Domaines: Intelligence artificielle [cs.AI]; Modélisation et simulation; Système multi-agents [cs.MA]

Boris Lesner, Bruno Zanuttini. Efficient Policy Construction for MDPs Represented in Probabilistic PDDL. Proc. 21st International Conference on Automated Planning and Scheduling (ICAPS 2011)), Jun 2011, Germany. 8 p. ⟨hal-00944350⟩