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 (Author), Liu, Miao (Contributor), Sivakumar, Kavinayan P (Contributor), Omidshafiei, Shayegan (Contributor), How, Jonathan P (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2018-04-13T22:28:08Z.
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