The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems
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.
Cite
Text
Oliehoek et al. "The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems." Machine Learning Open Source Software, 2017.Markdown
[Oliehoek et al. "The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems." Machine Learning Open Source Software, 2017.](https://mlanthology.org/mloss/2017/oliehoek2017jmlr-madp/)BibTeX
@article{oliehoek2017jmlr-madp,
title = {{The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems}},
author = {Oliehoek, Frans A. and Spaan, Matthijs T. J. and Terwijn, Bas and Robbel, Philipp and Messias, João V.},
journal = {Machine Learning Open Source Software},
year = {2017},
pages = {1-5},
volume = {18},
url = {https://mlanthology.org/mloss/2017/oliehoek2017jmlr-madp/}
}