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Towards graphical partially observable Monte-Carlo planning

: Pfrommer, J.

Fulltext urn:nbn:de:0011-n-4175749 (352 KByte PDF)
MD5 Fingerprint: 741af570a1ec8665c624459d1979ec14
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Created on: 25.10.2016

Beyerer, Jürgen (Ed.) ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung -IOSB-, Karlsruhe; Karlsruhe Institute of Technology -KIT-, Lehrstuhl für Interaktive Echtzeitsysteme -IES-:
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2015. Proceedings : July, 19 to 26, Triberg-Nussbach, Germany
Karlsruhe: KIT Scientific Publishing, 2016 (Karlsruher Schriften zur Anthropomatik 24)
ISBN: 978-3-7315-0519-8
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2015, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Sample-based online algorithms are state of the art for solving Partially Observable Markov Decision Problems (POMDP). But also the state of the art solver POMCP still suffers from the curse of dimensionality and curse of history. In Distributed POMDP, independent agents jointly optimise their actions under some coordination mechanism where every agent has access to a subset of the observations. In this work, we introduce Graphical POMDP (GPOMDP) drawing from existing Distributed POMDP appraoches as well as graph-based formulations as found in graphical probabilistic models. Further, we propose the Graphical POMCP (GPOMCP) algorithm that combines POMCP with message passing similar to the Belief Propagation (BP) algorithm from Graphical Probabilistic Models. In preliminary tests, GPOMCP shows good performance on a common Distributed POMDP benchmark.