Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

IROS, pp. 1853-1860, 2017.

Cited by: 0|Bibtex|Views9|DOI:https://doi.org/10.1109/iros.2017.8206001
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a general framework for cooperative sequential decision making under uncertainty and MAs allow temporally ext...More

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