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, 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. DownloadAbstractThis 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}, keywords = {workshop}, url = {https://www.aaai.org/ocs/index.php/FSS/FSS15/paper/view/11706/11508}, 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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