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ANDRIUSHCHENKO, R. ČEŠKA, M. JUNGES, S. KATOEN, J.
Original Title
Inductive Synthesis of Finite-State Controllers for POMDPs
Type
conference paper
Language
English
Original Abstract
We present a novel learning framework to obtain finite-state controllers (FSCs) for partially observable Markov decision processes and illustrate its applicability for indefinite-horizon specifications. Our framework builds on oracle-guided inductive synthesis to explore a design space compactly representing available FSCs. The inductive synthesis approach consists of two stages: The outer stage determines the design space, i.e., the set of FSC candidates, while the inner stage efficiently explores the design space. This framework is easily generalisable and shows promising results when compared to existing approaches. Experiments indicate that our technique is (i) competitive to state-of-the-art belief-based approaches for indefinite-horizon properties, (ii) yields smaller FSCs than existing methods for several POMDP models, and (iii) naturally treats multi-objective specifications.
Keywords
partially observable Markov decision processes, finite-state controllers, inductive synthesis, counter-examples, abstraction
Authors
ANDRIUSHCHENKO, R.; ČEŠKA, M.; JUNGES, S.; KATOEN, J.
Released
17. 6. 2022
Publisher
Proceedings of Machine Learning Research
Location
Eindhoven
ISBN
2640-3498
Year of study
180
Number
Pages from
85
Pages to
95
Pages count
11
BibTex
@inproceedings{BUT178215, author="ANDRIUSHCHENKO, R. and ČEŠKA, M. and JUNGES, S. and KATOEN, J.", title="Inductive Synthesis of Finite-State Controllers for POMDPs", booktitle="Conference on Uncertainty in Artificial Intelligence", year="2022", series="Proceedings of Machine Learning Research", volume="180", number="180", pages="85--95", publisher="Proceedings of Machine Learning Research", address="Eindhoven", issn="2640-3498" }