Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions

Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions

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...

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Bibliographic Details
Main Authors: Amato, Christopher, Liu, Miao, Sivakumar, Kavinayan P, Omidshafiei, Shayegan, How, Jonathan P
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Format: Article
Published: Institute of Electrical and Electronics Engineers (IEEE) 2018
Online Access:http://hdl.handle.net/1721.1/114739
https://orcid.org/0000-0002-1648-8325
https://orcid.org/0000-0003-0903-0137
https://orcid.org/0000-0001-8576-1930

Internet

http://hdl.handle.net/1721.1/114739
https://orcid.org/0000-0002-1648-8325
https://orcid.org/0000-0003-0903-0137
https://orcid.org/0000-0001-8576-1930

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