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, João V. Messias

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
21 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalJournal of Machine Learning Research
Issue number89
Publication statusPublished - Aug 2017


  • software
  • decision-theoretic planning
  • reinforcement learning
  • multiagent systems


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