Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent SystemsFrans 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. DownloadAbstractThis 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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