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The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems

Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, and João V. Messias. The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems. Journal of Machine Learning Research, 18(89):1–5, 2017.

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Abstract

This article describes the MultiAgent Decision Process (\MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent systems; provides a large number of models for decision-theoretic decision making, including one-shot and sequential decision making under various assumptions of observability and cooperation, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an extensive range of planning and learning algorithms for single- and multiagent systems; it is released under a GNU GPL v3 license; and is written in C++ and designed to be extensible via the object-oriented paradigm.

BibTeX Entry

@article{Oliehoek17JMLR,
    title =     {The {MADP} Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems},
    author =    {Frans A. Oliehoek and Matthijs T. J. Spaan and Bas Terwijn and
                 Philipp Robbel and {Jo\~{a}o}  V. Messias},
    journal =   JMLR,
    year =      2017,
    volume  =   {18},
    number  =   {89},
    pages   =   {1-5},
    url     =   {http://jmlr.org/papers/v18/17-156.html},
    keywords =   {refereed, journal},
    abstract = {
    This article describes the MultiAgent Decision Process (\MADP) toolbox, a
    software library to support planning and learning for intelligent agents
    and multiagent systems in uncertain environments. Key features are that it
    supports partially observable environments and stochastic transition
    models; has unified support for single- and multiagent systems; provides a
    large number of models for decision-theoretic decision making, including
    one-shot and sequential decision making under various assumptions of
    observability and cooperation, such as Dec-POMDPs and POSGs; provides tools
    and parsers to quickly prototype new problems; provides an extensive range
    of planning and learning algorithms for single- and multiagent systems; it
    is released under a GNU GPL v3 license; and is written in C++ and designed
    to be extensible via the object-oriented paradigm.
    }
}

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