Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions

IEEE International Conference on Robotics and Automation, 2015.

Cited by: 45|Bibtex|Views38|DOI:https://doi.org/10.1109/ICRA.2015.7140035
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow f...More

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