Search and Explore: Symbiotic Policy Synthesis in POMDPs

článek ve sborníku ve WoS nebo Scopus

angličtina

This paper marries two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making under uncertainty. A central issue is to find a POMDP controller - that solely decides based on the observations seen so far - to achieve a total expected reward objective. As finding optimal controllers is undecidable, we concentrate on synthesising good finite-state controllers (FSCs). We do so by tightly integrating two modern, orthogonal methods for POMDP controller synthesis: a belief-based and an inductive approach. The former method obtains an FSC from a finite fragment of the so-called belief MDP, an MDP that keeps track of the probabilities of equally observable POMDP states. The latter is an inductive search technique over a set of FSCs, e.g., controllers with a fixed memory size. The key result of this paper is a symbiotic anytime algorithm that tightly integrates both approaches such that each profits from the controllers constructed by the other. Experimental results indicate a substantial improvement in the value of the controllers while significantly reducing the synthesis time and memory footprint.

partially observable Markov decision processes, finite-state controllers, beliefs, inductive synthesis

ANDRIUSHCHENKO, R.; ALEXANDER, B.; ČEŠKA, M.; JUNGES, S.; KATOEN, J.; MACÁK, F.

2. 8. 2023

Springer Verlag

Cham

978-3-031-37708-2

Computer Aided Verification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

113

135

23