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, Jo\~{a}o V. Messias; 18(89):1−5, 2017.
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; is released under the GNU GPL v3 license; and is written in C++ and designed to be extensible via the object-oriented paradigm.
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