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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, Philipp Robbel, and João V. Messias. The MADP Toolbox: An Open-Source Library for Planning and Learning in (Multi-)Agent Systems. In Sequential Decision Making for Intelligent Agents---Papers from the AAAI 2015 Fall Symposium, pp. 59–62, November 2015.

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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 un- certain environments. Some of its key features are that it sup- ports partially observable environments and stochastic tran- sition models; has unified support for single- and multiagent systems; provides a large number of models for decision- theoretic decision making, including one-shot decision mak- ing (e.g., Bayesian games) and sequential decision mak- ing under various assumptions of observability and coopera- tion, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an ex- tensive range of planning and learning algorithms for single- and multiagent systems; and is written in C++ and designed to be extensible via the object-oriented paradigm.

BibTeX Entry

@inproceedings{Oliehoek15SDMIA,
    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 
                 Philipp Robbel and 
                 {Jo\~{a}o} V. Messias},
    oldbooktitle = {Proceedings of the {AAAI} Fall Symposium on Sequential Decision Making in Intelligent Agents},
    booktitle = {Sequential Decision Making for Intelligent Agents---Papers from the AAAI 2015 Fall Symposium},
    month =     nov,
    year =      2015,
    pages =     {59--62},
    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 un-
    certain environments. Some of its key features are that it sup-
    ports partially observable environments and stochastic tran-
    sition models; has unified support for single- and multiagent
    systems; provides a large number of models for decision-
    theoretic decision making, including one-shot decision mak-
    ing (e.g., Bayesian games) and sequential decision mak-
    ing under various assumptions of observability and coopera-
    tion, such as Dec-POMDPs and POSGs; provides tools and
    parsers to quickly prototype new problems; provides an ex-
    tensive range of planning and learning algorithms for single-
    and multiagent systems; and is written in C++ and designed
    to be extensible via the object-oriented paradigm.
    }
}

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