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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 =      {Journal of Machine Learning Research},
  volume =       18,
  number =       89,
  pages =        {1--5},
  year =         2017
}

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