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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
Volume18
Issue number89
StatePublished - Aug 2017

    Research areas

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

ID: 34681360